REFINING MACHINE LEARNING MODELS TO MITIGATE ADVERSARIAL ATTACKS IN AUTONOMOUS SYSTEMS AND APPLICATIONS

In various examples, a technique for processing sensor data includes generating, using a machine learning model and based on a first sensor data instance, a first set of confidences for a set of output types and a first adversarial confidence that represents a likelihood that the first sensor data instance is adversarial. The technique also includes determining that the first sensor data instance is adversarial based on the first adversarial confidence. The technique further includes transmitting a first indication that the first sensor data instance is adversarial to one or more downstream components such that the one or more downstream components perform one or more operations based at least on the indication.

BACKGROUND

A visual autonomous or semi-autonomous driving system analyzes sensor data to understand the environment around an autonomous or semi-autonomous vehicle (e.g., a self-driving car). These systems commonly use machine learning models that process the sensor data collected using sensors on the vehicle, and use the output thereof to perform various operations-such as to detect, classify, and/or track pedestrians, animals, buildings, road conditions, traffic signs, barriers, other vehicles, and/or other objects or scenarios around the vehicle. This output may be used to guide driving or control decisions to aid in operation of the vehicle in a safe manner.

In some instances, the operation of a visual driving system can be impeded by an adversarial attack that causes the machine learning models to generate incorrect, inaccurate, or imprecise predictions. For example, an adversarial attack can involve anywhere from a slight to a significant perturbation to the color, shape, text, or other visual attributes of an environmental feature-such as a traffic sign, a vehicle or other dynamic object, a road marking, etc. In an example with a traffic sign, these perturbations can manifest in a manner that causes a machine learning model to incorrectly categorize or classify the traffic sign (e.g., by predicting that the speed limit shown on the traffic sign is significantly higher or lower than the actual speed limit on the traffic sign). When such an adversarial attack succeeds, the incorrect prediction generated by the visual driving system can lead to improper decisions by one or more downstream components or systems of the vehicle.

Existing approaches for reducing the susceptibility of a visual driving system to adversarial attacks involve augmenting or enhancing a training dataset used to train a machine learning model prior to deployment in the visual driving system. These approaches can also, or instead, use an ensemble of multiple machine learning models to generate predictions that are subsequently used by the visual driving system to make or guide driving decisions. However, these techniques do not guarantee that the machine learning model(s) is able to defend against adversarial attacks manifested in sensor data, which are typically perturbed in specific ways to cause the machine learning model(s) to output incorrect predictions.

As such, a need exists for more effective techniques for improving the robustness of machine learning models—such as those included visual driving systems—to adversarial attacks.

SUMMARY

Embodiments of the present disclosure relate to techniques for refining machine learning models to mitigate adversarial attacks. The techniques described herein include generating, using a machine learning model and based on a first sensor data instance, a first set of confidences for a set of output types and a first adversarial confidence that represents a probability that the first sensor data instance is adversarial. The technique also includes determining that the first sensor data instance is adversarial based on the first adversarial confidence. The technique further includes transmitting a first indication that the first sensor data instance is adversarial to one or more downstream components such that the one or more downstream components may perform one or more operations based at least on the indication.

One technical advantage of the disclosed techniques relative to conventional solutions is that a machine learning model is able to detect, account for, and aid in the notification of adversarial attacks on the system. Accordingly, a system that uses the output of such a machine learning model may be safer and more resistant to adversarial attacks than conventional systems that include machine learning models that have not been trained to recognize and/or defend against adversarial attacks. Another technical advantage of the disclosed techniques is a reduction in computational overhead and resource consumption, compared with prior art approaches that use an ensemble of multiple machine learning models to guard against adversarial attacks.

DETAILED DESCRIPTION

Systems and methods are disclosed for mitigating adversarial attacks via refinement of machine learning models. Although the present disclosure may be described with respect to an example autonomous or semi-autonomous vehicle or machine500(alternatively referred to herein as “vehicle500” or “ego-500,” an example of which is described with respect toFIGS.5A-5D), this is not intended to be limiting. For example, the systems and methods described herein may be used by, without limitation, non-autonomous vehicles or machines, semi-autonomous vehicles or machines (e.g., in one or more adaptive driver assistance systems (ADAS)), autonomous vehicles or machines, piloted and un-piloted robots or robotic platforms, warehouse vehicles, off-road vehicles, vehicles coupled to one or more trailers, flying vessels, boats, shuttles, emergency response vehicles, motorcycles, electric or motorized bicycles, aircraft, construction vehicles, underwater craft, drones, and/or other vehicle types. In addition, although the present disclosure may be described with respect to mitigating adversarial attacks on machine learning models employed in autonomous or semi-autonomous vehicles, this is not intended to be limiting, and the systems and methods described herein may be used in augmented reality, virtual reality, mixed reality, robotics, security and surveillance, autonomous or semi-autonomous machine applications, and/or any other technology spaces where adversarial attacks may occur.

As discussed herein, the correct operation of a visual driving system in an autonomous or semi-autonomous vehicle can be affected by an adversarial attack that causes one or more machine learning models of the system to generate incorrect predictions. When such an adversarial attack succeeds, these incorrect predictions can cause the autonomous or semi-autonomous vehicle or machine (or any other system to which an adversarial attack is aimed) to perform one or more operations improperly or not at a desired or required level of accuracy or precision.

To improve the ability of visual driving systems to guard against adversarial attacks, the disclosed techniques perform fine-tuning of a pre-trained machine learning model using adversarial images (or other types of adversarial data, such as LiDAR data, RADAR data, ultrasonic data, and/or data generated using any other sensor type, such as but not limited to those described herein with respect to the vehicle500ofFIGS.5A-5D). The adversarial images can be generated by perturbing a set of original images based on one or more types of adversarial attack techniques. Once generated, the adversarial images may be processed using the pre-trained machine learning model, and the predictions generated using the pre-trained machine learning model may be compared to ground truth data (e.g., labels) corresponding to the original images from which the adversarial images were generated. Based on these comparisons, the adversarial images are divided into a “clean” dataset of adversarial images for which the pre-trained machine learning model generated correct predictions and an “adversarial” dataset of adversarial images for which the pre-trained machine learning model generated incorrect predictions.

The machine learning model is then refined using the dataset of adversarial images and corresponding ground truth. During the refinement process, adversarial images in the clean dataset may be processed using the machine learning model, and the machine learning model may be trained based on losses between predictions generated using the machine learning model based on the adversarial images and the ground truth for the original images that correspond to the adversarial images.

Adversarial images in the adversarial dataset may also be processed using the machine learning model, and the machine learning model may be trained based on losses between predictions generated using the machine learning model based on the adversarial images and ground truth data that is determined based on perturbation strengths associated with the adversarial images. More specifically, adversarial images that are in the adversarial dataset and associated with perturbation strengths of less than a threshold amount may be assigned labels for the corresponding original images, and adversarial images that are in the adversarial dataset and associated with perturbation strengths of greater than the threshold amount may be assigned an “adversarial label” that indicates that the corresponding images (or regions of images) are to be disregarded, processed differently, etc. when determining one or more actions to be performed using the output of the machine learning model in the visual driving system. The machine learning model may be trained based on losses between predictions generated using the machine learning model from the adversarial images and the corresponding ground truth labels.

After the machine learning model has been trained using the adversarial images and corresponding ground truth, the machine learning model can be deployed—e.g., in a visual driving system—to generate predictions for additional images. For example, the machine learning model may be used to detect and/or track objects in the vicinity of a self-driving car during operation of the self-driving car. The output of the machine learning model may be provided to one or more downstream components that generate commands and/or signals that are used to operate the self-driving car.

One technical advantage of the disclosed techniques relative to the conventional solutions is that a machine learning model is able to detect and, at least in some cases, aid in correcting for adversarial data that attempts to cause the machine learning model to operate incorrectly. Accordingly, a system that uses the output of the machine learning model may be safer and more resistant to adversarial attacks than conventional systems that include machine learning models that have not been trained to recognize and/or defend against adversarial attacks. Another technical advantage of the disclosed techniques is a reduction in computational overhead and resource consumption, compared with conventional approaches that use an ensemble of multiple machine learning models to guard against adversarial attacks.

FIG.1illustrates a computing device100configured to implement one or more aspects of various embodiments. In at least one embodiment, computing device100includes a desktop computer, a laptop computer, a smart phone, a personal digital assistant (PDA), a tablet computer, a server, one or more virtual machines, and/or any other type of computing device configured to receive input, process data, and optionally display images, and is suitable for practicing one or more embodiments. Computing device100is configured to run an evaluation engine122, a refinement engine124, and an execution engine126that may reside in a memory116. It is noted that the computing device described herein is illustrative and that any other technically feasible configurations fall within the scope of the present disclosure. For example, multiple instances of evaluation engine122, refinement engine124, and/or execution engine126may execute on a set of nodes in a distributed and/or cloud computing system to implement the functionality of computing device100.

In one embodiment, computing device100includes, without limitation, an interconnect (bus)112that connects one or more processors102, an input/output (I/O) device interface104coupled to one or more input/output (I/O) devices108, memory116, a storage114, and/or a network interface106. Processor(s)102may include any suitable processor implemented as a central processing unit (CPU), a graphics processing unit (GPU), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), an artificial intelligence (AI) accelerator, a parallel processing unit (PPU), a data processing unit (DPU), any other type of processing unit, or a combination of different processing units, such as a CPU(s) configured to operate in conjunction with a GPU(s). In general, processor(s)102may include any technically feasible hardware unit capable of processing data and/or executing software applications. Further, in the context of this disclosure, the computing elements shown in computing device100may correspond to a physical computing system (e.g., a system in a data center) and/or may correspond to a virtual computing instance executing within a computing cloud.

In at least one embodiment, I/O devices108include devices capable of receiving input, such as a keyboard, a mouse, a touchpad, a VR/MR/AR headset, a gesture recognition system, and/or a microphone, as well as devices capable of providing output, such as a display device and/or speaker. Additionally, I/O devices108may include devices capable of both receiving input and providing output, such as a touchscreen, a universal serial bus (USB) port, and so forth. I/O devices108may be configured to receive various types of input from an end-user (e.g., a designer) of computing device100, and to also provide various types of output to the end-user of computing device100, such as displayed digital images or digital videos or text. In some embodiments, one or more of I/O devices108are configured to couple computing device100to a network110.

In one embodiment, network110is any technically feasible type of communications network that allows data to be exchanged between computing device100and internal, local, remote, or external entities or devices, such as a web server or another networked computing device. For example, network110may include a wide area network (WAN), a local area network (LAN), a wireless (e.g., WiFi) network, and/or the Internet, among others.

In at least one embodiment, storage114includes non-volatile storage for applications and data, and may include fixed or removable disk drives, flash memory devices, and CD-ROM, DVD-ROM, Blu-Ray, HD-DVD, or other magnetic, optical, or solid-state storage devices. Evaluation engine122, refinement engine124, and/or execution engine126may be stored in storage114and loaded into memory116when executed.

In one embodiment, memory116includes a random-access memory (RAM) module, a flash memory unit, and/or any other type of memory unit or combination thereof. Processor(s)102, I/O device interface104, and network interface106may be configured to read data from and write data to memory116. Memory116may include various software programs that can be executed by processor(s)102and application data associated with said software programs, including evaluation engine122, refinement engine124, and/or execution engine126.

Evaluation engine122includes functionality to evaluate the performance of a machine learning model in generating predictions from adversarial examples. For example, evaluation engine122may generate a set of adversarial images by applying various types of perturbations to a set of original (e.g., captured, synthetic, etc.) images. Evaluation engine122may input each adversarial image into a neural network that detects objects in images. Evaluation engine122may also compare the output generated using the neural network with a corresponding ground truth data (e.g., a label, annotation, etc.) corresponding to the original image from which the adversarial image was generated. Evaluation engine122may then divide the adversarial images into two or more datasets. A first dataset may include adversarial images for which the neural network correctly predicted the output for the corresponding original image, and a second dataset may include adversarial images for which the neural network did not correctly predict the output for the corresponding original image.

Refinement engine124performs additional training and/or fine-tuning of the machine learning model using the datasets generated using the evaluation engine122. Continuing with the above example, the refinement engine124may train (e.g., update one or more parameters of) the neural network in a way that minimizes a first loss between predictions generated using the machine learning model based on adversarial images in the first dataset and the ground truth (labels) for the corresponding original images. Refinement engine124may train the neural network to predict different labels for different subsets of adversarial images in the second dataset. More specifically, refinement engine124may divide the second dataset into a first subset of adversarial images associated with perturbation strengths that exceed a threshold amount and a second subset of adversarial images associated with perturbation strengths below a threshold amount (which may be the same or different threshold as the adversarial images). Refinement engine124may train the neural network in a way that minimizes a second loss between predictions generated using the machine learning model from adversarial images in the first subset and an “adversarial label” indicating that the images (or regions of images) are to be disregarded or processed differently (e.g., by ignoring portions of the images identified as adversarial) when determining actions to be performed by one or more downstream components. Refinement engine124may additionally train the neural network in a way that minimizes a third loss between predictions generated using the machine learning model from adversarial images in the second subset and the labels or other ground truth for the corresponding original images.

Execution engine126uses the trained machine learning model to generate predictions for additional data. For example, execution engine126may deploy and execute the trained machine learning model within an autonomous or semi-autonomous software driving stack (“drive stack”) of an autonomous or semi-autonomous machine. During operation of the machine, the trained machine learning model may be used to perform any of various operations, such as to detect and track objects in images captured using cameras of the machine. The output of the machine learning model may be provided to one or more downstream components (e.g., control components, behavior planning components, actuation components, world model management components, etc.) that use the output to generate one or more commands and/or make other decisions related to the operation of the machine. Consequently, evaluation engine122, refinement engine124, and execution engine126may be used to improve the robustness of machine learning models and/or, more generally, driving systems, against adversarial attacks, as discussed in further detail herein.

FIG.2is a more detailed illustration of evaluation engine122, refinement engine124, and execution engine126ofFIG.1, according to various embodiments. As mentioned above, evaluation engine122, refinement engine124, and execution engine126operate to improve the robustness of a machine learning model208against adversarial attacks. Each of these components is described in further detail herein.

In some embodiments, the machine learning model208includes a pre-trained model that is used to generate predictions related to a set of images232. For example, machine learning model208may include one or more recurrent neural networks (RNNs), convolutional neural networks (CNNs), deep neural networks (DNNs), deep convolutional networks (DCNs), residual neural networks (ResNets), graph neural networks, autoencoders, transformer neural networks, deep stereo geometry networks (DSGNs), stereo R-CNNs, and/or other types of artificial neural networks or components of artificial neural networks. Machine learning model208may also, or instead, include a regression model, support vector machine, decision tree, random forest, gradient-boosted tree, naïve Bayes classifier, Bayesian network, Hidden Markov model (HMM), hierarchical model, ensemble model, clustering technique, and/or another type of machine learning model that does not utilize artificial neural network components. Machine learning model208may be pre-trained to generate outputs that may be used to perform any of various operations, such as to detect and/or track objects, detect and/or classify road conditions, and/or perform other functions with respect to the images232. Machine learning model208may also, or instead, be pre-trained to aid in the prediction of trajectories, paths, distances, and/or behaviors of vehicles, pedestrians, and/or other moving or dynamic objects. Machine learning model208may also, or instead, be pre-trained to aid in generating a trajectory for navigating an autonomous or semi-autonomous vehicle or machine, given a destination, available routes to the destination, detected obstacles, and/or other criteria.

As mentioned herein, evaluation engine122analyzes the performance of machine learning model208in processing a set of adversarial images204. As shown inFIG.2, evaluation engine122generates adversarial images204by applying a set of perturbations202to a set of original images210. For example, evaluation engine122may use a set of known adversarial attack techniques (e.g., fast gradient step method (FGSM), projected gradient descent (PGD), limited-memory Broyden-Fletcher-Goldfarm-Shannon (L-BGFS), Carlini & Wagner, Jacobian-based Saliance Map Attack (JSMA), DeepFool, etc.) to apply perturbations202to original images210in order to generate a corresponding set of adversarial images204. Evaluation engine122may also, or instead, use perturbations202that are specified, selected, or defined by one or more users to convert original images210into adversarial images204. Evaluation engine122may also, or instead, apply multiple perturbations202to a single original image to generate a corresponding adversarial image, interpolate between two or more perturbations202to generate a perturbation that is applied to the original image, and/or otherwise combine multiple perturbations202to generate the adversarial image from the original image.

Perturbations202include changes to pixel values in original images210that can cause machine learning model208to generate incorrect predictions212. For example, perturbations202may include changes to pixel values in original images210that are imperceptible to humans, but that are designed to cause machine learning model208to generate incorrect or imprecise outputs-such as to incorrectly predict the locations and/or classes associated with objects in original images210. In another example, perturbations202may include changes to pixel values in certain “patches” or “regions” of original images210that can cause machine learning models208to detect ghost objects, mis-classify objects in original images210, and/or otherwise generate incorrect or imprecise predictions212. In a third example, perturbations202may include changes to original images210that are both perceptible to humans and capable of causing machine learning model208to generate incorrect predictions212. In a fourth example, perturbations202may include extensive changes to original images210that would cause both humans and machine learning models208to incorrectly predict object classes, object locations, and/or other attributes associated with objects in original images210.

In one or more embodiments, original images210include images from a training dataset for machine learning model208. For example, in an automotive use case, original images210may include images that depict roads, traffic signs, vehicles, pedestrians, intersections, lanes, obstructions, and/or other objects in the vicinity of a vehicle. Evaluation engine122, refinement engine124, execution engine126, and/or another component may train machine learning model208to detect and/or track objects in original images210based on bounding shapes, classes, and/or other labels206for the objects.

Alternatively, original images210may include images that have not been used to train the machine learning model208. For example, in the automotive use case, original images210may include images of roads, traffic signs, vehicles, pedestrians, intersections, lanes, obstructions, and/or other objects that are distinct from those depicted in the images within the training dataset for machine learning model208. Original images210may also, or instead, include augmentations of the images within the training dataset, synthetic images, and/or other types of images that are visually distinct from those in the training dataset.

After generating adversarial images204that include perturbations202of original images210, evaluation engine122may input or apply the adversarial images204(e.g., image data or sensor data representative thereof, either with or without pre-processing) into the machine learning model208. Evaluation engine122compares the output generated using the machine learning model208from each adversarial image with one or more labels206(or other ground truth data) for the original image from which the adversarial image was generated. Evaluation engine122uses the results of the comparison to identify a set of incorrect predictions212made using machine learning model208from a first subset of adversarial images204and a set of correct predictions214made using the machine learning model208from a second subset of adversarial images.

For example, evaluation engine122may input individual (or as a batch) adversarial images in the set of adversarial images204into the machine learning model208, the machine learning model208may process the image or sensor data, and the system may obtain an output from the machine learning model208—e.g., representative of predicted probabilities for a set of object classes as the corresponding output of machine learning model208. Evaluation engine122may compare the object class with the highest predicted probability to a label (or other ground truth data type) for the corresponding original image. Evaluation engine122may also verify that the highest predicted probability meets or exceeds a threshold. When the object class with the highest predicted probability matches the label and/or the highest predicted probability meets or exceeds the threshold, evaluation engine122may determine that machine learning model208has made a correct prediction with respect to the adversarial image. When the object class with the highest predicted probability does not match the label and/or the highest predicted probability does not meet or exceed the threshold, evaluation engine122may determine that machine learning model208has made an incorrect prediction with respect to the adversarial image. Although described as outputting probabilities, other output types are within the scope of the present disclosure. For example, the machine learning model208may output confidences, percentages, binary outputs (e.g.,0or1), and/or may regress on one or more output values.

Evaluation engine122uses incorrect predictions212and correct predictions214to generate two datasets associated with adversarial images204. As shown inFIG.2, evaluation engine122generates an adversarial dataset216that includes a set of adversarial images220for which the machine learning model208has generated incorrect predictions212. Evaluation engine122also generates a clean dataset218that includes a different set of adversarial images220for which machine learning model has generated correct predictions214. Adversarial images220and222thus correspond to two disjoint subsets of the set of adversarial images204inputted into machine learning model208.

Evaluation engine122may populate clean dataset218and adversarial dataset216with various labels or other ground truth data types associated with the corresponding adversarial images220and222. More specifically, evaluation engine122associates adversarial images222in clean dataset218with original labels226for the corresponding original images210. Thus, clean dataset218includes adversarial images222that correspond to perturbed versions of a subset of original images210, as well as original labels226that correspond to a subset of labels206for those original images210.

To add labels to adversarial dataset216, evaluation engine122determines perturbation strengths240associated with adversarial images220in adversarial dataset216. Each perturbation strength represents a measure or indication of the extent by which an original image was perturbed to generate a corresponding adversarial image. For example, evaluation engine122may compute each perturbation strength as the number or proportion of pixels in the original image that were perturbed to generate the adversarial image, the extent to which pixel values in the original image have been perturbed to generate the adversarial image, and/or another measure of the amount of perturbation applied to the original image to generate the adversarial image. In another example, evaluation engine122may compute a perturbation strength as a measure of the difference between the prediction outputted by machine learning model208from a given adversarial image and a label for the corresponding original image. In a third example, evaluation engine122may compute a perturbation strength as a weighted combination of multiple measures and/or indications of the amount of perturbation associated with a corresponding adversarial image.

In some embodiments, perturbation strengths240are determined based on user input associated with human review of adversarial images220and/or the corresponding original images210. For example, evaluation engine122may present one or more adversarial images220and a list of possible object classes for objects in the adversarial image(s) to one or more users. Each user may select the most likely object class for an object depicted in a given adversarial image. Each user may also, or instead, indicate that the object class for the object cannot be determined, given the appearance of the object in the adversarial image. An object class that is correctly identified by the user would indicate a low perturbation strength associated with the adversarial image. An object class that is incorrectly identified by the user and/or an object that is flagged by the user as having an indeterminable object class would indicate a high perturbation strength. Each user may also, or instead, provide a score, rating, and/or other input indicating the level of perturbation associated with the adversarial image (e.g., low, medium, high, etc.), given a comparison of the adversarial image with the original image from which the adversarial image was generated. When multiple users provide input related to the perturbation strength associated with a given adversarial image, the input may be averaged and/or otherwise aggregated into an overall user-specified indication of perturbation strength for the adversarial image.

After perturbation strengths240are computed and/or determined for adversarial images220, evaluation engine122uses one or more thresholds for perturbation strengths240to assign labels to adversarial images220within adversarial dataset216. More specifically, adversarial dataset216includes a first subset of adversarial images220that are associated with a set of original labels224for the corresponding original images210. Adversarial dataset216also includes a second subset of adversarial images220that are associated with a set of adversarial labels228.

To assign adversarial labels228and/or original labels224to adversarial images220, evaluation engine122compares the perturbation strength for individual adversarial images in adversarial images220to a perturbation strength threshold. If the perturbation strength does not meet or exceed the threshold, evaluation engine122retrieves, from the set of labels206for the set of original images210, a label for the original image from which the adversarial image was generated. Evaluation engine122adds the label to adversarial dataset216(e.g., as part of the set of original labels224) and associates the label with the adversarial image (e.g., by mapping the adversarial image to the label within adversarial dataset216). In other words, evaluation engine122identifies a first subset of adversarial images220that are associated with incorrect predictions212of the machine learning model208and perturbation strengths240that do not meet or do not exceed the threshold (or another threshold). Evaluation engine122then adds the first subset of adversarial images220and original labels224that include a subset of labels206for the corresponding original images210to adversarial dataset216.

If the perturbation strength for a given adversarial image in adversarial images220meets or exceeds the threshold, evaluation engine122assigns an adversarial label (e.g., in adversarial labels228) to the adversarial image within adversarial dataset216. This adversarial label corresponds to an adversarial class that indicates that the adversarial image (or an object in the adversarial image) has been perturbed or corrupted to such an extent that the adversarial image (or object) is to be ignored or processed differently (e.g., the downstream component(s) may receive an indication of the adversarial nature of the data used to generate the output, and the downstream component(s) may perform operations in accordance with the indication) that includes the machine learning model208.

In some embodiments, evaluation engine122generates adversarial labels228and/or original labels224within adversarial dataset216based on multiple thresholds for perturbation strengths240. For example, evaluation engine122may compare computed and/or user-generated perturbation strengths240for adversarial images220with three or more thresholds or values. If an adversarial image has a computed perturbation strength that is lower than all the thresholds and/or includes a user-specified perturbation strength of low, evaluation engine122may generate a corresponding label that includes a value of 1 for a corresponding original label and a value of 0 for the adversarial label. If an adversarial image has a computed perturbation strength that is higher than all the thresholds and/or a user-specified perturbation strength of high, evaluation engine122may generate a label for the that includes a value of 0 for a corresponding original label and a value of 1 for the adversarial label.

Continuing with the above example, if an adversarial image has a computed perturbation strength that falls between two thresholds and/or a user-specified perturbation strength that falls between low and high, evaluation engine122may generate a corresponding label that includes a first nonzero value for a corresponding original label and a second nonzero value for the adversarial label, where the two nonzero values sum to 1. Thus, an adversarial image with a “medium low” perturbation strength may have a label with a value of 0.75 for the corresponding original label and a value of 0.25 for the adversarial label, an adversarial image with a “medium” perturbation strength may have a label with a value of 0.5 for both the corresponding original label and the adversarial label, and an adversarial image with a “medium high” perturbation strength may have a label with a value of 0.25 for the corresponding original label and a value of 0.75 for the adversarial label. Numeric values assigned to the adversarial label and original label for a given adversarial image thus represent probabilities or confidences in the object belonging to the corresponding classes. In some embodiments, where the output of the machine learning model indicates a degree or value indicating the adversarial nature of the sensor data, the downstream component(s) may perform operations differently based on the degree or value (e.g., more adversarial, disregard the output, less adversarial, use the output but weight less heavily).

After clean dataset218and adversarial dataset216are generated, refinement engine124uses clean dataset218and adversarial dataset216to perform additional training and/or fine-tuning of machine learning model208. More specifically, refinement engine124inputs adversarial images220from adversarial dataset216into machine learning model208and obtains corresponding adversarial dataset predictions260from machine learning model208. Adversarial dataset predictions260include numeric confidences representing predicted probabilities (or other output types) of various classes (or other prediction types) represented by labels206, as well as a numeric confidence representing a predicted probability of the adversarial label for the corresponding adversarial images220. Refinement engine124computes a mean squared error, cross entropy loss, and/or one or more other losses264between the outputted confidences and the corresponding adversarial labels228and/or original labels224for adversarial images220. Refinement engine124then uses a training technique (e.g., gradient descent and backpropagation) to update model parameters230(e.g., weights and biases) of machine learning model208in a way that reduces the computed losses264.

Refinement engine124also inputs each of adversarial images222from clean dataset218into machine learning model208and obtains corresponding clean dataset predictions262from machine learning model208. Clean dataset predictions262may also include numeric confidences representing predicted probabilities of various classes represented by labels206, as well as a numeric confidence representing a predicted probability of the adversarial label for the corresponding adversarial images222. Refinement engine124computes a mean squared error, cross entropy loss, and/or one or more other losses266between the outputted confidences and the corresponding original labels226for adversarial images222. Refinement engine124additionally uses a training technique (e.g., gradient descent and backpropagation) to update model parameters230of machine learning model208in a way that reduces the computed losses264.

Consequently, refinement engine124performs one or more training stages that train machine learning model208to predict original labels226associated with adversarial images222in clean dataset218. This training of machine learning model208using clean dataset218allows machine learning model208to continue generating correct predictions214for adversarial images222and/or other images that are similar to adversarial images222.

Refinement engine124also performs one or more training stages that train machine learning model208to predict original labels224and/or adversarial labels228associated with adversarial images220in adversarial dataset216. This training of machine learning model208using adversarial dataset216allows machine learning model208to learn to predict original labels224for adversarial images220associated with lower perturbation strengths240, thereby correcting the predictive output of the machine learning model208with respect to these types of adversarial images220and/or images that are similar to these types of adversarial images220. This training of model208using adversarial dataset216additionally allows machine learning model208to learn to predict adversarial labels228for adversarial images220(and/or other images that are similar to adversarial images220) associated with higher perturbation strengths240, thereby providing machine learning model208with the ability to recognize and flag adversarial data that may otherwise be used to disrupt the operation of machine learning model208and/or downstream components that use the output of machine learning model208.

After model parameters230of machine learning model208have been fine-tuned by refinement engine124based on losses264-266, execution engine126uses the fine-tuned machine learning model208to generate adversarial labels234and/or non-adversarial labels236for additional images232that are not in adversarial dataset216or clean dataset218. For example, execution engine126may deploy and execute the machine learning model208within a corresponding system, such as an autonomous or semi-autonomous driving system. During operation of the autonomous or semi-autonomous vehicle or machine, images232of the environment around the autonomous vehicle may be captured using one or more cameras of the machine or vehicle, as described in further detail herein with respect toFIGS.5A-5D. The captured images232may be input or applied to the machine learning model208, the machine learning model208may process the data, and the output of the machine learning model208may be used to perform any of a variety of operations, such as to detect and track objects in the vicinity of a vehicle or machine. The output of the machine learning model208may include non-adversarial labels236that correspond to object classes, bounding shapes, semantic segmentations, predicted paths, and/or other output types. The output of machine learning model208may also, or instead, include adversarial labels234that indicate that the corresponding images232and/or regions of images232include adversarial data, and an indication, message, or signal indicating the same may be generated as a result.

Execution engine126also uses adversarial labels234and non-adversarial labels236to make certain decisions and/or perform certain actions. Continuing with the above example, execution engine126may provide adversarial labels234and non-adversarial labels236computed using the machine learning model208to one or more downstream components that use the output-such as to generate driving commands and/or make other decisions related to the operation of the autonomous or semi-autonomous machine or vehicle. Because the downstream components are aware of images232and/or regions of images232that include adversarial data, the downstream components are able to account for the adversarial nature of the data and to operate in view of this information. As a result, the machine learning model(s)208may be robust to adversarial attacks.

While the operation of evaluation engine122, refinement engine124, and execution engine126ofFIG.1has been described above primarily with respect to image data and autonomous or semi-autonomous driving, it will be appreciated that evaluation engine122, refinement engine124, and execution engine126can be used to analyze and improve the performance of machine learning models208in resisting adversarial attacks for other types of data (e.g., LiDAR, RADAR, ultrasonic, etc.), scenarios, and/or use cases. For example, evaluation engine122, refinement engine124, and execution engine126may be used to guard against adversarial attacks that involve perturbations or modifications to other types of sensor data (as described in further detail below with respect toFIGS.5A-5C), point cloud data, audio data, video data, text data, time-series data, network packets, source code, and/or other types of data. In another example, evaluation engine122, refinement engine124, and execution engine126may be used to improve the robustness of machine learning model208and downstream components against adversarial attacks that attempt to cause machine learning model208to generate incorrect predictions212related to spam filtering, detecting cybersecurity attacks, biometric recognition, medical imaging, identity fraud, and/or other applications or use cases.

It should be understood that this and other arrangements described herein are set forth only as examples. Other arrangements and elements (e.g., machines, interfaces, functions, orders, groupings of functions, etc.) may be used in addition to or instead of those shown, and some elements may be omitted altogether. Further, many of the elements described herein are functional entities that may be implemented as discrete or distributed components or in conjunction with other components, and in any suitable combination and location. Various functions described herein as being performed by entities may be carried out by hardware, firmware, and/or software. For instance, various functions may be carried out by a processor executing instructions stored in memory. In some embodiments, the systems, methods, and processes described herein may be executed using similar components, features, and/or functionality to those of example autonomous vehicle500ofFIGS.5A-5D, example computing device600ofFIG.6, and/or example data center700ofFIG.7.

FIG.3illustrates a flow diagram of a method300for fine-tuning a machine learning model using adversarial data, according to various embodiments. As shown inFIG.3, method300begins with operation302, in which evaluation engine122applies one or more perturbations to a set of sensor data instances to generate a set of perturbed sensor data instances. For example, evaluation engine122may use one or more known adversarial attack techniques, one or more user-specified adversarial attack techniques, and/or a combination of adversarial attack techniques to perturb a set of “original images” captured using one or more cameras and/or synthetically generated within a simulation environment of a simulation system. The perturbed images correspond to adversarial images that can cause the machine learning model to generate incorrect predictions.

At operation304, evaluation engine122inputs the perturbed sensor data instances into the machine learning model to generate a set of predictions associated with the perturbed sensor data instances. Continuing with the above example, evaluation engine122may process the adversarial images using a neural network to generate predictions that include, as non-limiting examples, types of objects, locations of the objects, paths of the objects, behaviors associated with the objects, and/or other attributes related to objects depicted in the adversarial images.

At operation306, evaluation engine122compares the predictions to labels for the corresponding sensor data instances. Continuing with the above example, evaluation engine122may compare each prediction output using the neural network from an adversarial image to a label or other ground truth data type for a corresponding original image to determine if the prediction is correct or incorrect.

At operation308, evaluation engine122generates a clean dataset that includes perturbed sensor data instances associated with correct predictions of the machine learning model and labels for the corresponding sensor instances. Continuing with the above example, evaluation engine122may populate the clean dataset with adversarial images for which the neural network correctly predicted the labels for the corresponding original images. Evaluation engine122may also associate the adversarial images with the labels within the clean dataset.

At operation310, evaluation engine122generates an adversarial dataset that includes perturbed sensor instances associated with incorrect predictions of the machine learning model. Continuing with the above example, evaluation engine122may populate the adversarial dataset with adversarial images for which the neural network did not correctly predict the labels for the corresponding images.

At operation312, evaluation engine122determines labels for the perturbed sensor data instances in the adversarial dataset based on perturbation strengths associated with the perturbed sensor data instances. Continuing with the above example, evaluation engine122may determine a perturbation strength for individual adversarial images in the adversarial dataset based on, without limitation, the number or proportion of pixels in the corresponding original image that have been perturbed, the extent to which the pixels in the original image have been perturbed, the difference between the prediction generated by the neural network from the adversarial image and the corresponding label, user input indicating the extent to which the pixels in the original image have been perturbed, and/or user input indicating the extent to which the perturbation of the original image affects a human's ability to correctly predict the label for the original image. Evaluation engine122may use one or more thresholds and/or values associated with the perturbation strength to assign one or more labels to the adversarial image. When the threshold(s) and/or value(s) indicate that the adversarial image is associated with a low perturbation strength, evaluation engine122may generate a label for the adversarial image that indicates a high confidence in the label for the original image. As the perturbation strength increases, evaluation engine122may lower the confidence in the label for the original image and increase the confidence in an adversarial label that indicates that the adversarial image (or an object in the adversarial image) has been perturbed or corrupted to such an extent that the adversarial image (or object) is to be disregarded and/or processed differently than if there were no adversarial nature or less than some threshold of adversarial features in the images.

At operation314, refinement engine124updates parameters of the machine learning model based on one or more losses between the predictions generated using the machine learning model from the perturbed sensor data instances in the clean and adversarial datasets and the corresponding labels. Continuing with the above example, refinement engine124may input the adversarial images from both datasets into the neural network. For individual adversarial images, refinement engine124may obtain output from the neural network that includes, as a non-limiting example, predicted confidences that an object in the adversarial image belongs to various object classes and/or an adversarial class associated with the adversarial label. Refinement engine124may compute a mean squared error, cross entropy loss, and/or one or more other losses between the outputted confidences and one or more corresponding labels for the adversarial image in the dataset. Refinement engine124may then use a training technique (e.g., gradient descent and backpropagation) to update the weights of the neural network in a way that reduces the computed losses. Refinement engine124may continue training the neural network using the clean dataset and adversarial dataset until the weights converge; the loss(es) fall below a threshold; a certain number of training steps, iterations, batches, epochs, and/or training stages has been performed; and/or another condition is met.

Now referring toFIG.4,FIG.4illustrates a flow diagram of a method400for processing sensor data, according to various embodiments. As shown inFIG.4, method400begins with operation402, in which execution engine126generates, using a machine learning model and based on a sensor data instance, a set of confidences for a set of output types and an adversarial confidence that represents a likelihood that the sensor data instance is adversarial. For example, execution engine126may input an image into a neural network and receive the set of confidences and adversarial confidence as a corresponding output of the neural network.

At operation404, execution engine126determines a predicted type for the sensor data instance based on the set of confidences and the adversarial confidence. For example, execution engine126may set the predicted type to the output type with the highest confidence. If the adversarial confidence is higher than all of the other confidences, execution engine may set the predicted type to “adversarial.” Execution engine126may also verify that the highest confidence meets a threshold before determining the predicted type. If the highest confidence does not meet the threshold, execution engine126may set the predicted type to “unknown.”

At operation406, execution engine126transmits an indication of the predicted type to one or more downstream components. For example, execution engine126may transmit the predicted type to downstream components that use the output of the machine learning model to generate driving commands and/or make other decisions related to the operation of an autonomous vehicle. Execution engine126may also, or instead, transmit the set of confidences and/or adversarial confidence outputted by the machine learning model to the downstream components. The downstream components would use the predicted type, set of confidences, and/or the adversarial confidence to evaluate and/or respond to the risk of an adversarial attack on the autonomous or semi-autonomous machine or vehicle.

Example Autonomous Vehicle

The vehicle500may include components such as a chassis, a vehicle body, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and other components of a vehicle. The vehicle500may include a propulsion system550, such as an internal combustion engine, hybrid electric power plant, an all-electric engine, and/or another propulsion system type. The propulsion system550may be connected to a drive train of the vehicle500, which may include a transmission, to enable the propulsion of the vehicle500. The propulsion system550may be controlled in response to receiving signals from the throttle/accelerator552.

A steering system554, which may include a steering wheel, may be used to steer the vehicle500(e.g., along a desired path or route) when the propulsion system550is operating (e.g., when the vehicle is in motion). The steering system554may receive signals from a steering actuator556. The steering wheel may be optional for full automation (Level 5) functionality.

The brake sensor system546may be used to operate the vehicle brakes in response to receiving signals from the brake actuators548and/or brake sensors.

Controller(s)536, which may include one or more system on chips (SoCs)504(FIG.5C) and/or GPU(s), may provide signals (e.g., representative of commands) to one or more components and/or systems of the vehicle500. For example, the controller(s) may send signals to operate the vehicle brakes via one or more brake actuators548, to operate the steering system554via one or more steering actuators556, to operate the propulsion system550via one or more throttle/accelerators552. The controller(s)536may include one or more onboard (e.g., integrated) computing devices (e.g., supercomputers) that process sensor signals, and output operation commands (e.g., signals representing commands) to enable autonomous driving and/or to assist a human driver in driving the vehicle500. The controller(s)536may include a first controller536for autonomous driving functions, a second controller536for functional safety functions, a third controller536for artificial intelligence functionality (e.g., computer vision), a fourth controller536for infotainment functionality, a fifth controller536for redundancy in emergency conditions, and/or other controllers. In some examples, a single controller536may handle two or more of the above functionalities, two or more controllers536may handle a single functionality, and/or any combination thereof.

The controller(s)536may provide the signals for controlling one or more components and/or systems of the vehicle500in response to sensor data received from one or more sensors (e.g., sensor inputs). The sensor data may be received from, for example and without limitation, global navigation satellite systems (“GNSS”) sensor(s)558(e.g., Global Positioning System sensor(s)), RADAR sensor(s)560, ultrasonic sensor(s)562, LIDAR sensor(s)564, inertial measurement unit (IMU) sensor(s)566(e.g., accelerometer(s), gyroscope(s), magnetic compass(es), magnetometer(s), etc.), microphone(s)596, stereo camera(s)568, wide-view camera(s)570(e.g., fisheye cameras), infrared camera(s)572, surround camera(s)574(e.g., 360 degree cameras), long-range and/or mid-range camera(s)598, speed sensor(s)544(e.g., for measuring the speed of the vehicle500), vibration sensor(s)542, steering sensor(s)540, brake sensor(s) (e.g., as part of the brake sensor system546), and/or other sensor types.

In some embodiments, the controller(s)536receive sensor data from one or more types of sensors and use one or more machine learning models to generate predictions related to the sensor data. These machine learning model(s) can be trained and/or refined to be resistant to adversarial attacks using the components and/or systems ofFIGS.1-2. The controller(s)536can use the predictions to generate commands or signals that are used to operate the vehicle500.

One or more of the controller(s)536may receive inputs (e.g., represented by input data) from an instrument cluster532of the vehicle500and provide outputs (e.g., represented by output data, display data, etc.) via a human-machine interface (HMI) display534, an audible annunciator, a loudspeaker, and/or via other components of the vehicle500. The outputs may include information such as vehicle velocity, speed, time, map data (e.g., the High Definition (“HD”) map522ofFIG.5C), location data (e.g., the vehicle's500location, such as on a map), direction, location of other vehicles (e.g., an occupancy grid), information about objects and status of objects as perceived by the controller(s)536, etc. For example, the HMI display534may display information about the presence of one or more objects (e.g., a street sign, caution sign, traffic light changing, etc.), and/or information about driving maneuvers the vehicle has made, is making, or will make (e.g., changing lanes now, taking exit34B in two miles, etc.).

The vehicle500further includes a network interface524which may use one or more wireless antenna(s)526and/or modem(s) to communicate over one or more networks. For example, the network interface524may be capable of communication over Long-Term Evolution (“LTE”), Wideband Code Division Multiple Access (“WCDMA”), Universal Mobile Telecommunications System (“UMTS”), Global System for Mobile communication (“GSM”), IMT-CDMA Multi-Carrier (“CDMA2000”), etc. The wireless antenna(s)526may also enable communication between objects in the environment (e.g., vehicles, mobile devices, etc.), using local area network(s), such as Bluetooth, Bluetooth Low Energy (“LE”), Z-Wave, ZigBee, etc., and/or low power wide-area network(s) (“LPWANs”), such as LoRaWAN, SigFox, etc.

FIG.5Bis an example of camera locations and fields of view for the example autonomous vehicle500ofFIG.5A, in accordance with some embodiments of the present disclosure. The cameras and respective fields of view are one example embodiment and are not intended to be limiting. For example, additional and/or alternative cameras may be included and/or the cameras may be located at different locations on the vehicle500.

A variety of cameras may be used in a front-facing configuration, including, for example, a monocular camera platform that includes a complementary metal oxide semiconductor (“CMOS”) color imager. Another example may be a wide-view camera(s)570that may be used to perceive objects coming into view from the periphery (e.g., pedestrians, crossing traffic or bicycles). Although only one wide-view camera is illustrated inFIG.5B, there may be any number (including zero) of wide-view cameras570on the vehicle500. In addition, any number of long-range camera(s)598(e.g., a long-view stereo camera pair) may be used for depth-based object detection, especially for objects for which a neural network has not yet been trained. The long-range camera(s)598may also be used for object detection and classification, as well as basic object tracking.

Any number of stereo cameras568may also be included in a front-facing configuration. In at least one embodiment, one or more of stereo camera(s)568may include an integrated control unit comprising a scalable processing unit, which may provide a programmable logic (“FPGA”) and a multi-core micro-processor with an integrated Controller Area Network (“CAN”) or Ethernet interface on a single chip. Such a unit may be used to generate a 3D map of the vehicle's environment, including a distance estimate for all the points in the image. An alternative stereo camera(s)568may include a compact stereo vision sensor(s) that may include two camera lenses (one each on the left and right) and an image processing chip that may measure the distance from the vehicle to the target object and use the generated information (e.g., metadata) to activate the autonomous emergency braking and lane departure warning functions. Other types of stereo camera(s)568may be used in addition to, or alternatively from, those described herein.

Cameras with a field of view that include portions of the environment to the side of the vehicle500(e.g., side-view cameras) may be used for surround view, providing information used to create and update the occupancy grid, as well as to generate side impact collision warnings. For example, surround camera(s)574(e.g., four surround cameras574as illustrated inFIG.5B) may be positioned to on the vehicle500. The surround camera(s)574may include wide-view camera(s)570, fisheye camera(s), 360 degree camera(s), and/or the like. Four example, four fisheye cameras may be positioned on the vehicle's front, rear, and sides. In an alternative arrangement, the vehicle may use three surround camera(s)574(e.g., left, right, and rear), and may leverage one or more other camera(s) (e.g., a forward-facing camera) as a fourth surround view camera.

Cameras with a field of view that include portions of the environment to the rear of the vehicle500(e.g., rear-view cameras) may be used for park assistance, surround view, rear collision warnings, and creating and updating the occupancy grid. A wide variety of cameras may be used including, but not limited to, cameras that are also suitable as a front-facing camera(s) (e.g., long-range and/or mid-range camera(s)598, stereo camera(s)568), infrared camera(s)572, etc.), as described herein.

Each of the components, features, and systems of the vehicle500inFIG.5Care illustrated as being connected via bus502. The bus502may include a Controller Area Network (CAN) data interface (alternatively referred to herein as a “CAN bus”). A CAN may be a network inside the vehicle500used to aid in control of various features and functionality of the vehicle500, such as actuation of brakes, acceleration, braking, steering, windshield wipers, etc. A CAN bus may be configured to have dozens or even hundreds of nodes, each with its own unique identifier (e.g., a CAN ID). The CAN bus may be read to find steering wheel angle, ground speed, engine revolutions per minute (RPMs), button positions, and/or other vehicle status indicators. The CAN bus may be ASIL B compliant.

Although the bus502is described herein as being a CAN bus, this is not intended to be limiting. For example, in addition to, or alternatively from, the CAN bus, FlexRay and/or Ethernet may be used. Additionally, although a single line is used to represent the bus502, this is not intended to be limiting. For example, there may be any number of busses502, which may include one or more CAN busses, one or more FlexRay busses, one or more Ethernet busses, and/or one or more other types of busses using a different protocol. In some examples, two or more busses502may be used to perform different functions, and/or may be used for redundancy. For example, a first bus502may be used for collision avoidance functionality and a second bus502may be used for actuation control. In any example, each bus502may communicate with any of the components of the vehicle500, and two or more busses502may communicate with the same components. In some examples, each SoC504, each controller536, and/or each computer within the vehicle may have access to the same input data (e.g., inputs from sensors of the vehicle500), and may be connected to a common bus, such the CAN bus.

The vehicle500may include one or more controller(s)536, such as those described herein with respect toFIG.5A. The controller(s)536may be used for a variety of functions. The controller(s)536may be coupled to any of the various other components and systems of the vehicle500, and may be used for control of the vehicle500, artificial intelligence of the vehicle500, infotainment for the vehicle500, and/or the like.

The vehicle500may include a system(s) on a chip (SoC)504. The SoC504may include CPU(s)506, GPU(s)508, processor(s)510, cache(s)512, accelerator(s)514, data store(s)516, and/or other components and features not illustrated. The SoC(s)504may be used to control the vehicle500in a variety of platforms and systems. For example, the SoC(s)504may be combined in a system (e.g., the system of the vehicle500) with an HD map522which may obtain map refreshes and/or updates via a network interface524from one or more servers (e.g., server(s)578ofFIG.5D).

The CPU(s)506may include a CPU cluster or CPU complex (alternatively referred to herein as a “CCPLEX”). The CPU(s)506may include multiple cores and/or L2 caches. For example, in some embodiments, the CPU(s)506may include eight cores in a coherent multi-processor configuration. In some embodiments, the CPU(s)506may include four dual-core clusters where each cluster has a dedicated L2 cache (e.g., a 2 MB L2 cache). The CPU(s)506(e.g., the CCPLEX) may be configured to support simultaneous cluster operation enabling any combination of the clusters of the CPU(s)506to be active at any given time.

The GPU(s)508may include an integrated GPU (alternatively referred to herein as an “iGPU”). The GPU(s)508may be programmable and may be efficient for parallel workloads. The GPU(s)508, in some examples, may use an enhanced tensor instruction set. The GPU(s)508may include one or more streaming microprocessors, where each streaming microprocessor may include an L1 cache (e.g., an L1 cache with at least 96 KB storage capacity), and two or more of the streaming microprocessors may share an L2 cache (e.g., an L2 cache with a 512 KB storage capacity). In some embodiments, the GPU(s)508may include at least eight streaming microprocessors. The GPU(s)508may use compute application programming interface(s) (API(s)). In addition, the GPU(s)508may use one or more parallel computing platforms and/or programming models (e.g., NVIDIA's CUDA).

The GPU(s)508may include unified memory technology including access counters to allow for more accurate migration of memory pages to the processor that accesses them most frequently, thereby improving efficiency for memory ranges shared between processors. In some examples, address translation services (ATS) support may be used to allow the GPU(s)508to access the CPU(s)506page tables directly. In such examples, when the GPU(s)508memory management unit (MMU) experiences a miss, an address translation request may be transmitted to the CPU(s)506. In response, the CPU(s)506may look in its page tables for the virtual-to-physical mapping for the address and transmits the translation back to the GPU(s)508. As such, unified memory technology may allow a single unified virtual address space for memory of both the CPU(s)506and the GPU(s)508, thereby simplifying the GPU(s)508programming and porting of applications to the GPU(s)508.

In addition, the GPU(s)508may include an access counter that may keep track of the frequency of access of the GPU(s)508to memory of other processors. The access counter may help ensure that memory pages are moved to the physical memory of the processor that is accessing the pages most frequently.

The SoC(s)504may include any number of cache(s)512, including those described herein. For example, the cache(s)512may include an L3 cache that is available to both the CPU(s)506and the GPU(s)508(e.g., that is connected both the CPU(s)506and the GPU(s)508). The cache(s)512may include a write-back cache that may keep track of states of lines, such as by using a cache coherence protocol (e.g., MEI, MESI, MSI, etc.). The L3 cache may include 4 MB or more, depending on the embodiment, although smaller cache sizes may be used.

The SoC(s)504may include an arithmetic logic unit(s) (ALU(s)) which may be leveraged in performing processing with respect to any of the variety of tasks or operations of the vehicle500—such as processing DNNs. In addition, the SoC(s)504may include a floating point unit(s) (FPU(s))—or other math coprocessor or numeric coprocessor types—for performing mathematical operations within the system. For example, the SoC(s)504may include one or more FPUs integrated as execution units within a CPU(s)506and/or GPU(s)508.

The SoC(s)504may include one or more accelerators514(e.g., hardware accelerators, software accelerators, or a combination thereof). For example, the SoC(s)504may include a hardware acceleration cluster that may include optimized hardware accelerators and/or large on-chip memory. The large on-chip memory (e.g., 4 MB of SRAM), may enable the hardware acceleration cluster to accelerate neural networks and other calculations. The hardware acceleration cluster may be used to complement the GPU(s)508and to off-load some of the tasks of the GPU(s)508(e.g., to free up more cycles of the GPU(s)508for performing other tasks). As an example, the accelerator(s)514may be used for targeted workloads (e.g., perception, convolutional neural networks (CNNs), etc.) that are stable enough to be amenable to acceleration. The term “CNN,” as used herein, may include all types of CNNs, including region-based or regional convolutional neural networks (RCNNs) and Fast RCNNs (e.g., as used for object detection). These CNNs can be trained and/or refined to be resistant to adversarial attacks using the components and/or systems ofFIGS.1-2.

The DLA(s) may perform any function of the GPU(s)508, and by using an inference accelerator, for example, a designer may target either the DLA(s) or the GPU(s)508for any function. For example, the designer may focus processing of CNNs and floating point operations on the DLA(s) and leave other functions to the GPU(s)508and/or other accelerator(s)514.

The SoC(s)504may include data store(s)516(e.g., memory). The data store(s)516may be on-chip memory of the SoC(s)504, which may store neural networks to be executed on the GPU and/or the DLA. In some examples, the data store(s)516may be large enough in capacity to store multiple instances of neural networks for redundancy and safety. The data store(s)512may comprise L2 or L3 cache(s)512. Reference to the data store(s)516may include reference to the memory associated with the PVA, DLA, and/or other accelerator(s)514, as described herein.

The SoC(s)504may include one or more processor(s)510(e.g., embedded processors). The processor(s)510may include a boot and power management processor that may be a dedicated processor and subsystem to handle boot power and management functions and related security enforcement. The boot and power management processor may be a part of the SoC(s)504boot sequence and may provide runtime power management services. The boot power and management processor may provide clock and voltage programming, assistance in system low power state transitions, management of SoC(s)504thermals and temperature sensors, and/or management of the SoC(s)504power states. Each temperature sensor may be implemented as a ring-oscillator whose output frequency is proportional to temperature, and the SoC(s)504may use the ring-oscillators to detect temperatures of the CPU(s)506, GPU(s)508, and/or accelerator(s)514. If temperatures are determined to exceed a threshold, the boot and power management processor may enter a temperature fault routine and put the SoC(s)504into a lower power state and/or put the vehicle500into a chauffeur to safe stop mode (e.g., bring the vehicle500to a safe stop).

The processor(s)510may further include a real-time camera engine that may include a dedicated processor subsystem for handling real-time camera management.

The processor(s)510may further include a high-dynamic range signal processor that may include an image signal processor that is a hardware engine that is part of the camera processing pipeline.

The video image compositor may also be configured to perform stereo rectification on input stereo lens frames. The video image compositor may further be used for user interface composition when the operating system desktop is in use, and the GPU(s)508is not required to continuously render new surfaces. Even when the GPU(s)508is powered on and active doing 3D rendering, the video image compositor may be used to offload the GPU(s)508to improve performance and responsiveness.

The SoC(s)504may further include a broad range of peripheral interfaces to enable communication with peripherals, audio codecs, power management, and/or other devices. The SoC(s)504may be used to process data from cameras (e.g., connected over Gigabit Multimedia Serial Link and Ethernet), sensors (e.g., LIDAR sensor(s)564, RADAR sensor(s)560, etc. that may be connected over Ethernet), data from bus502(e.g., speed of vehicle500, steering wheel position, etc.), data from GNSS sensor(s)558(e.g., connected over Ethernet or CAN bus). The SoC(s)504may further include dedicated high-performance mass storage controllers that may include their own DMA engines, and that may be used to free the CPU(s)506from routine data management tasks.

The SoC(s)504may be an end-to-end platform with a flexible architecture that spans automation levels 3-5, thereby providing a comprehensive functional safety architecture that leverages and makes efficient use of computer vision and ADAS techniques for diversity and redundancy, provides a platform for a flexible, reliable driving software stack, along with deep learning tools. The SoC(s)504may be faster, more reliable, and even more energy-efficient and space-efficient than conventional systems. For example, the accelerator(s)514, when combined with the CPU(s)506, the GPU(s)508, and the data store(s)516, may provide for a fast, efficient platform for level 3-5 autonomous vehicles.

As another example, multiple neural networks may be run simultaneously, as is required for Level 3, 4, or 5 driving. For example, a warning sign consisting of “Caution: flashing lights indicate icy conditions,” along with an electric light, may be independently or collectively interpreted by several neural networks. The sign itself may be identified as a traffic sign by a first deployed neural network (e.g., a neural network that has been trained), the text “Flashing lights indicate icy conditions” may be interpreted by a second deployed neural network, which informs the vehicle's path planning software (preferably executing on the CPU Complex) that when flashing lights are detected, icy conditions exist. The flashing light may be identified by operating a third deployed neural network over multiple frames, informing the vehicle's path-planning software of the presence (or absence) of flashing lights. All three neural networks may run simultaneously, such as within the DLA and/or on the GPU(s)508. All three neural networks may also be trained and/or refined to be robust against adversarial attacks using the systems and/or componentsFIGS.1-2.

In some examples, a CNN for facial recognition and vehicle owner identification may use data from camera sensors to identify the presence of an authorized driver and/or owner of the vehicle500. The always on sensor processing engine may be used to unlock the vehicle when the owner approaches the driver door and turn on the lights, and, in security mode, to disable the vehicle when the owner leaves the vehicle. In this way, the SoC(s)504provide for security against theft and/or carjacking.

The vehicle may include a CPU(s)518(e.g., discrete CPU(s), or dCPU(s)), that may be coupled to the SoC(s)504via a high-speed interconnect (e.g., PCIe). The CPU(s)518may include an X86 processor, for example. The CPU(s)518may be used to perform any of a variety of functions, including arbitrating potentially inconsistent results between ADAS sensors and the SoC(s)504, and/or monitoring the status and health of the controller(s)536and/or infotainment SoC530, for example.

The vehicle500may include a GPU(s)520(e.g., discrete GPU(s), or dGPU(s)), that may be coupled to the SoC(s)504via a high-speed interconnect (e.g., NVIDIA's NVLINK). The GPU(s)520may provide additional artificial intelligence functionality, such as by executing redundant and/or different neural networks, and may be used to train and/or update neural networks based on input (e.g., sensor data) from sensors of the vehicle500.

The vehicle500may further include the network interface524which may include one or more wireless antennas526(e.g., one or more wireless antennas for different communication protocols, such as a cellular antenna, a Bluetooth antenna, etc.). The network interface524may be used to enable wireless connectivity over the Internet with the cloud (e.g., with the server(s)578and/or other network devices), with other vehicles, and/or with computing devices (e.g., client devices of passengers). To communicate with other vehicles, a direct link may be established between the two vehicles and/or an indirect link may be established (e.g., across networks and over the Internet). Direct links may be provided using a vehicle-to-vehicle communication link. The vehicle-to-vehicle communication link may provide the vehicle500information about vehicles in proximity to the vehicle500(e.g., vehicles in front of, on the side of, and/or behind the vehicle500). This functionality may be part of a cooperative adaptive cruise control functionality of the vehicle500.

The vehicle500may further include data store(s)528which may include off-chip (e.g., off the SoC(s)504) storage. The data store(s)528may include one or more storage elements including RAM, SRAM, DRAM, VRAM, Flash, hard disks, and/or other components and/or devices that may store at least one bit of data.

The vehicle500may further include GNSS sensor(s)558. The GNSS sensor(s)558(e.g., GPS, assisted GPS sensors, differential GPS (DGPS) sensors, etc.), to assist in mapping, perception, occupancy grid generation, and/or path planning functions. Any number of GNSS sensor(s)558may be used, including, for example and without limitation, a GPS using a USB connector with an Ethernet to Serial (RS-232) bridge.

The vehicle500may further include RADAR sensor(s)560. The RADAR sensor(s)560may be used by the vehicle500for long-range vehicle detection, even in darkness and/or severe weather conditions. RADAR functional safety levels may be ASIL B. The RADAR sensor(s)560may use the CAN and/or the bus502(e.g., to transmit data generated by the RADAR sensor(s)560) for control and to access object tracking data, with access to Ethernet to access raw data in some examples. A wide variety of RADAR sensor types may be used. For example, and without limitation, the RADAR sensor(s)560may be suitable for front, rear, and side RADAR use. In some example, Pulse Doppler RADAR sensor(s) are used.

The vehicle500may further include ultrasonic sensor(s)562. The ultrasonic sensor(s)562, which may be positioned at the front, back, and/or the sides of the vehicle500, may be used for park assist and/or to create and update an occupancy grid. A wide variety of ultrasonic sensor(s)562may be used, and different ultrasonic sensor(s)562may be used for different ranges of detection (e.g., 2.5 m, 4 m). The ultrasonic sensor(s)562may operate at functional safety levels of ASIL B.

The vehicle500may include LIDAR sensor(s)564. The LIDAR sensor(s)564may be used for object and pedestrian detection, emergency braking, collision avoidance, and/or other functions. The LIDAR sensor(s)564may be functional safety level ASIL B. In some examples, the vehicle500may include multiple LIDAR sensors564(e.g., two, four, six, etc.) that may use Ethernet (e.g., to provide data to a Gigabit Ethernet switch).

The vehicle may further include IMU sensor(s)566. The IMU sensor(s)566may be located at a center of the rear axle of the vehicle500, in some examples. The IMU sensor(s)566may include, for example and without limitation, an accelerometer(s), a magnetometer(s), a gyroscope(s), a magnetic compass(es), and/or other sensor types. In some examples, such as in six-axis applications, the IMU sensor(s)566may include accelerometers and gyroscopes, while in nine-axis applications, the IMU sensor(s)566may include accelerometers, gyroscopes, and magnetometers.

In some embodiments, the IMU sensor(s)566may be implemented as a miniature, high performance GPS-Aided Inertial Navigation System (GPS/INS) that combines micro-electro-mechanical systems (MEMS) inertial sensors, a high-sensitivity GPS receiver, and advanced Kalman filtering algorithms to provide estimates of position, velocity, and attitude. As such, in some examples, the IMU sensor(s)566may enable the vehicle500to estimate heading without requiring input from a magnetic sensor by directly observing and correlating the changes in velocity from GPS to the IMU sensor(s)566. In some examples, the IMU sensor(s)566and the GNSS sensor(s)558may be combined in a single integrated unit.

The vehicle may include microphone(s)596placed in and/or around the vehicle500. The microphone(s)596may be used for emergency vehicle detection and identification, among other things.

The vehicle may further include any number of camera types, including stereo camera(s)568, wide-view camera(s)570, infrared camera(s)572, surround camera(s)574, long-range and/or mid-range camera(s)598, and/or other camera types. The cameras may be used to capture image data around an entire periphery of the vehicle500. The types of cameras used depends on the embodiments and requirements for the vehicle500, and any combination of camera types may be used to provide the necessary coverage around the vehicle500. In addition, the number of cameras may differ depending on the embodiment. For example, the vehicle may include six cameras, seven cameras, ten cameras, twelve cameras, and/or another number of cameras. The cameras may support, as an example and without limitation, Gigabit Multimedia Serial Link (GMSL) and/or Gigabit Ethernet. Each of the camera(s) is described with more detail herein with respect toFIG.5AandFIG.5B.

The vehicle500may further include vibration sensor(s)542. The vibration sensor(s)542may measure vibrations of components of the vehicle, such as the axle(s). For example, changes in vibrations may indicate a change in road surfaces. In another example, when two or more vibration sensors542are used, the differences between the vibrations may be used to determine friction or slippage of the road surface (e.g., when the difference in vibration is between a power-driven axle and a freely rotating axle).

The vehicle500may include an ADAS system538. The ADAS system538may include a SoC, in some examples. The ADAS system538may include autonomous/adaptive/automatic cruise control (ACC), cooperative adaptive cruise control (CACC), forward crash warning (FCW), automatic emergency braking (AEB), lane departure warnings (LDW), lane keep assist (LKA), blind spot warning (BSW), rear cross-traffic warning (RCTW), collision warning systems (CWS), lane centering (LC), and/or other features and functionality.

The ACC systems may use RADAR sensor(s)560, LIDAR sensor(s)564, and/or a camera(s). The ACC systems may include longitudinal ACC and/or lateral ACC. Longitudinal ACC monitors and controls the distance to the vehicle immediately ahead of the vehicle500and automatically adjust the vehicle speed to maintain a safe distance from vehicles ahead. Lateral ACC performs distance keeping, and advises the vehicle500to change lanes when necessary. Lateral ACC is related to other ADAS applications such as LCA and CWS.

LKA systems are a variation of LDW systems. LKA systems provide steering input or braking to correct the vehicle500if the vehicle500starts to exit the lane.

The vehicle500may further include the infotainment SoC530(e.g., an in-vehicle infotainment system (IVI)). Although illustrated and described as a SoC, the infotainment system may not be a SoC, and may include two or more discrete components. The infotainment SoC530may include a combination of hardware and software that may be used to provide audio (e.g., music, a personal digital assistant, navigational instructions, news, radio, etc.), video (e.g., TV, movies, streaming, etc.), phone (e.g., hands-free calling), network connectivity (e.g., LTE, Wi-Fi, etc.), and/or information services (e.g., navigation systems, rear-parking assistance, a radio data system, vehicle related information such as fuel level, total distance covered, brake fuel level, oil level, door open/close, air filter information, etc.) to the vehicle500. For example, the infotainment SoC530may radios, disk players, navigation systems, video players, USB and Bluetooth connectivity, carputers, in-car entertainment, Wi-Fi, steering wheel audio controls, hands free voice control, a heads-up display (HUD), an HMI display534, a telematics device, a control panel (e.g., for controlling and/or interacting with various components, features, and/or systems), and/or other components. The infotainment SoC530may further be used to provide information (e.g., visual and/or audible) to a user(s) of the vehicle, such as information from the ADAS system538, autonomous driving information such as planned vehicle maneuvers, trajectories, surrounding environment information (e.g., intersection information, vehicle information, road information, etc.), and/or other information.

The infotainment SoC530may include GPU functionality. The infotainment SoC530may communicate over the bus502(e.g., CAN bus, Ethernet, etc.) with other devices, systems, and/or components of the vehicle500. In some examples, the infotainment SoC530may be coupled to a supervisory MCU such that the GPU of the infotainment system may perform some self-driving functions in the event that the primary controller(s)536(e.g., the primary and/or backup computers of the vehicle500) fail. In such an example, the infotainment SoC530may put the vehicle500into a chauffeur to safe stop mode, as described herein.

The vehicle500may further include an instrument cluster532(e.g., a digital dash, an electronic instrument cluster, a digital instrument panel, etc.). The instrument cluster532may include a controller and/or supercomputer (e.g., a discrete controller or supercomputer). The instrument cluster532may include a set of instrumentation such as a speedometer, fuel level, oil pressure, tachometer, odometer, turn indicators, gearshift position indicator, seat belt warning light(s), parking-brake warning light(s), engine-malfunction light(s), airbag (SRS) system information, lighting controls, safety system controls, navigation information, etc. In some examples, information may be displayed and/or shared among the infotainment SoC530and the instrument cluster532. In other words, the instrument cluster532may be included as part of the infotainment SoC530, or vice versa.

FIG.5Dis a system diagram for communication between cloud-based server(s) and the example autonomous vehicle500ofFIG.5A, in accordance with some embodiments of the present disclosure. The system576may include server(s)578, network(s)590, and vehicles, including the vehicle500. The server(s)578may include a plurality of GPUs584(A)-584(H) (collectively referred to herein as GPUs584), PCIe switches582(A)-582(H) (collectively referred to herein as PCIe switches582), and/or CPUs580(A)-580(B) (collectively referred to herein as CPUs580). The GPUs584, the CPUs580, and the PCIe switches may be interconnected with high-speed interconnects such as, for example and without limitation, NVLink interfaces588developed by NVIDIA and/or PCIe connections586. In some examples, the GPUs584are connected via NVLink and/or NVSwitch SoC and the GPUs584and the PCIe switches582are connected via PCIe interconnects. Although eight GPUs584, two CPUs580, and two PCIe switches are illustrated, this is not intended to be limiting. Depending on the embodiment, each of the server(s)578may include any number of GPUs584, CPUs580, and/or PCIe switches. For example, the server(s)578may each include eight, sixteen, thirty-two, and/or more GPUs584.

The server(s)578may receive, over the network(s)590and from the vehicles, image data representative of images showing unexpected or changed road conditions, such as recently commenced road-work. The server(s)578may transmit, over the network(s)590and to the vehicles, neural networks592, updated neural networks592, and/or map information594, including information regarding traffic and road conditions. The updates to the map information594may include updates for the HD map522, such as information regarding construction sites, potholes, detours, flooding, and/or other obstructions. In some examples, the neural networks592, the updated neural networks592, and/or the map information594may have resulted from new training and/or experiences represented in data received from any number of vehicles in the environment, and/or based on training performed at a datacenter (e.g., using the server(s)578and/or other servers). For example, the neural networks592, the updated neural networks592, and/or the map information594may be trained, refined, and/or updated to detect and guard against adversarial attacks using the systems and/or techniques described above with respect toFIGS.1-4.

The server(s)578may be used to train machine learning models (e.g., machine learning model208ofFIG.2, neural networks, etc.) based on training data. The training data may be generated by the vehicles, and/or may be generated in a simulation (e.g., using a game engine). In some examples, the training data is tagged (e.g., where the neural network benefits from supervised learning) and/or undergoes other pre-processing, while in other examples the training data is not tagged and/or pre-processed (e.g., where the neural network does not require supervised learning). In some embodiments, the training data includes clean dataset218and/or adversarial dataset216ofFIG.2. Training may be executed according to any one or more classes of machine learning techniques, including, without limitation, classes such as: supervised training, semi-supervised training, unsupervised training, self-learning, reinforcement learning, federated learning, transfer learning, feature learning (including principal component and cluster analyses), multi-linear subspace learning, manifold learning, representation learning (including spare dictionary learning), rule-based machine learning, anomaly detection, adversarial attack recognition, and any variants or combinations therefor. Once the machine learning models are trained, the machine learning models may be used by the vehicles (e.g., transmitted to the vehicles over the network(s)590, and/or the machine learning models may be used by the server(s)578to remotely monitor the vehicles.

In some examples, the server(s)578may receive data from the vehicles and apply the data to up-to-date real-time neural networks for real-time intelligent inferencing. The server(s)578may include deep-learning supercomputers and/or dedicated AI computers powered by GPU(s)584, such as a DGX and DGX Station machines developed by NVIDIA. However, in some examples, the server(s)578may include deep learning infrastructure that use only CPU-powered datacenters.

The deep-learning infrastructure of the server(s)578may be capable of fast, real-time inferencing, and may use that capability to evaluate and verify the health of the processors, software, and/or associated hardware in the vehicle500. For example, the deep-learning infrastructure may receive periodic updates from the vehicle500, such as a sequence of images and/or objects that the vehicle500has located in that sequence of images (e.g., via computer vision and/or other machine learning object classification techniques). The deep-learning infrastructure may run its own neural network to identify the objects and compare them with the objects identified by the vehicle500and, if the results do not match and the infrastructure concludes that the AI in the vehicle500is malfunctioning, the server(s)578may transmit a signal to the vehicle500instructing a fail-safe computer of the vehicle500to assume control, notify the passengers, and complete a safe parking maneuver.

For inferencing, the server(s)578may include the GPU(s)584and one or more programmable inference accelerators (e.g., NVIDIA's TensorRT). The combination of GPU-powered servers and inference acceleration may make real-time responsiveness possible. In other examples, such as where performance is less critical, servers powered by CPUs, FPGAS, and other processors may be used for inferencing.

Example Computing Device

FIG.6is a block diagram of an example computing device(s)600suitable for use in implementing some embodiments of the present disclosure. Computing device600may include an interconnect system602that directly or indirectly couples the following devices: memory604, one or more central processing units (CPUs)606, one or more graphics processing units (GPUs)608, a communication interface610, input/output (I/O) ports612, input/output components614, a power supply616, one or more presentation components618(e.g., display(s)), and one or more logic units620. In at least one embodiment, the computing device(s)600may comprise one or more virtual machines (VMs), and/or any of the components thereof may comprise virtual components (e.g., virtual hardware components). For non-limiting examples, one or more of the GPUs608may comprise one or more vGPUs, one or more of the CPUs606may comprise one or more vCPUs, and/or one or more of the logic units620may comprise one or more virtual logic units. As such, a computing device(s)600may include discrete components (e.g., a full GPU dedicated to the computing device600), virtual components (e.g., a portion of a GPU dedicated to the computing device600), or a combination thereof.

Although the various blocks ofFIG.6are shown as connected via the interconnect system602with lines, this is not intended to be limiting and is for clarity only. For example, in some embodiments, a presentation component618, such as a display device, may be considered an I/O component614(e.g., if the display is a touch screen). As another example, the CPUs606and/or GPUs608may include memory (e.g., the memory604may be representative of a storage device in addition to the memory of the GPUs608, the CPUs606, and/or other components). In other words, the computing device ofFIG.6is merely illustrative. Distinction is not made between such categories as “workstation,” “server,” “laptop,” “desktop,” “tablet,” “client device,” “mobile device,” “hand-held device,” “game console,” “electronic control unit (ECU),” “virtual reality system,” and/or other device or system types, as all are contemplated within the scope of the computing device ofFIG.6.

The interconnect system602may represent one or more links or busses, such as an address bus, a data bus, a control bus, or a combination thereof. The interconnect system602may include one or more bus or link types, such as an industry standard architecture (ISA) bus, an extended industry standard architecture (EISA) bus, a video electronics standards association (VESA) bus, a peripheral component interconnect (PCI) bus, a peripheral component interconnect express (PCIe) bus, and/or another type of bus or link. In some embodiments, there are direct connections between components. As an example, the CPU606may be directly connected to the memory604. Further, the CPU606may be directly connected to the GPU608. Where there is direct, or point-to-point connection between components, the interconnect system602may include a PCIe link to carry out the connection. In these examples, a PCI bus need not be included in the computing device600.

The CPU(s)606may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device600to perform one or more of the methods and/or processes described herein. The CPU(s)606may each include one or more cores (e.g., one, two, four, eight, twenty-eight, seventy-two, etc.) that are capable of handling a multitude of software threads simultaneously. The CPU(s)606may include any type of processor, and may include different types of processors depending on the type of computing device600implemented (e.g., processors with fewer cores for mobile devices and processors with more cores for servers). For example, depending on the type of computing device600, the processor may be an Advanced RISC Machines (ARM) processor implemented using Reduced Instruction Set Computing (RISC) or an x86 processor implemented using Complex Instruction Set Computing (CISC). The computing device600may include one or more CPUs606in addition to one or more microprocessors or supplementary co-processors, such as math co-processors.

In addition to or alternatively from the CPU(s)606, the GPU(s)608may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device600to perform one or more of the methods and/or processes described herein. One or more of the GPU(s)608may be an integrated GPU (e.g., with one or more of the CPU(s)606and/or one or more of the GPU(s)608may be a discrete GPU. In embodiments, one or more of the GPU(s)608may be a coprocessor of one or more of the CPU(s)606. The GPU(s)608may be used by the computing device600to render graphics (e.g., 3D graphics) or perform general purpose computations. For example, the GPU(s)608may be used for General-Purpose computing on GPUs (GPGPU). The GPU(s)608may include hundreds or thousands of cores that are capable of handling hundreds or thousands of software threads simultaneously. The GPU(s)608may generate pixel data for output images in response to rendering commands (e.g., rendering commands from the CPU(s)606received via a host interface). The GPU(s)608may include graphics memory, such as display memory, for storing pixel data or any other suitable data, such as GPGPU data. The display memory may be included as part of the memory604. The GPU(s)608may include two or more GPUs operating in parallel (e.g., via a link). The link may directly connect the GPUs (e.g., using NVLINK) or may connect the GPUs through a switch (e.g., using NVSwitch). When combined together, each GPU608may generate pixel data or GPGPU data for different portions of an output or for different outputs (e.g., a first GPU for a first image and a second GPU for a second image). Each GPU may include its own memory, or may share memory with other GPUs.

In addition to or alternatively from the CPU(s)606and/or the GPU(s)608, the logic unit(s)620may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device600to perform one or more of the methods and/or processes described herein. In embodiments, the CPU(s)606, the GPU(s)608, and/or the logic unit(s)620may discretely or jointly perform any combination of the methods, processes and/or portions thereof. One or more of the logic units620may be part of and/or integrated in one or more of the CPU(s)606and/or the GPU(s)608and/or one or more of the logic units620may be discrete components or otherwise external to the CPU(s)606and/or the GPU(s)608. In embodiments, one or more of the logic units620may be a coprocessor of one or more of the CPU(s)606and/or one or more of the GPU(s)608.

The communication interface610may include one or more receivers, transmitters, and/or transceivers that enable the computing device600to communicate with other computing devices via an electronic communication network, included wired and/or wireless communications. The communication interface610may include components and functionality to enable communication over any of a number of different networks, such as wireless networks (e.g., Wi-Fi, Z-Wave, Bluetooth, Bluetooth LE, ZigBee, etc.), wired networks (e.g., communicating over Ethernet or InfiniBand), low-power wide-area networks (e.g., LoRaWAN, SigFox, etc.), and/or the Internet. In one or more embodiments, logic unit(s)620and/or communication interface610may include one or more data processing units (DPUs) to transmit data received over a network and/or through interconnect system602directly to (e.g., a memory of) one or more GPU(s)608.

The I/O ports612may enable the computing device600to be logically coupled to other devices including the I/O components614, the presentation component(s)618, and/or other components, some of which may be built in to (e.g., integrated in) the computing device600. Illustrative I/O components614include a microphone, mouse, keyboard, joystick, game pad, game controller, satellite dish, scanner, printer, wireless device, etc. The I/O components614may provide a natural user interface (NUI) that processes air gestures, voice, or other physiological inputs generated by a user. In some instances, inputs may be transmitted to an appropriate network element for further processing. An NUI may implement any combination of speech recognition, stylus recognition, facial recognition, biometric recognition, gesture recognition both on screen and adjacent to the screen, air gestures, head and eye tracking, and touch recognition (as described in more detail below) associated with a display of the computing device600. The computing device600may be include depth cameras, such as stereoscopic camera systems, infrared camera systems, RGB camera systems, touchscreen technology, and combinations of these, for gesture detection and recognition. Additionally, the computing device600may include accelerometers or gyroscopes (e.g., as part of an inertia measurement unit (IMU)) that enable detection of motion. In some examples, the output of the accelerometers or gyroscopes may be used by the computing device600to render immersive augmented reality or virtual reality.

The power supply616may include a hard-wired power supply, a battery power supply, or a combination thereof. The power supply616may provide power to the computing device600to enable the components of the computing device600to operate.

The presentation component(s)618may include a display (e.g., a monitor, a touch screen, a television screen, a heads-up-display (HUD), other display types, or a combination thereof), speakers, and/or other presentation components. The presentation component(s)618may receive data from other components (e.g., the GPU(s)608, the CPU(s)606, DPUs, etc.), and output the data (e.g., as an image, video, sound, etc.).

Example Data Center

FIG.7illustrates an example data center700that may be used in at least one embodiments of the present disclosure. The data center700may include a data center infrastructure layer710, a framework layer720, a software layer730, and/or an application layer740.

As shown inFIG.7, the data center infrastructure layer710may include a resource orchestrator712, grouped computing resources714, and node computing resources (“node C.R.s”)716(1)-716(N), where “N” represents any whole, positive integer. In at least one embodiment, node C.R.s716(1)-716(N) may include, but are not limited to, any number of central processing units (CPUs) or other processors (including DPUs, accelerators, field programmable gate arrays (FPGAs), graphics processors or graphics processing units (GPUs), etc.), memory devices (e.g., dynamic read-only memory), storage devices (e.g., solid state or disk drives), network input/output (NW I/O) devices, network switches, virtual machines (VMs), power modules, and/or cooling modules, etc. In some embodiments, one or more node C.R.s from among node C.R.s716(1)-716(N) may correspond to a server having one or more of the above-mentioned computing resources. In addition, in some embodiments, the node C.R.s716(1)-7161(N) may include one or more virtual components, such as vGPUs, vCPUs, and/or the like, and/or one or more of the node C.R.s716(1)-716(N) may correspond to a virtual machine (VM).

In at least one embodiment, grouped computing resources714may include separate groupings of node C.R.s716housed within one or more racks (not shown), or many racks housed in data centers at various geographical locations (also not shown). Separate groupings of node C.R.s716within grouped computing resources714may include grouped compute, network, memory or storage resources that may be configured or allocated to support one or more workloads. In at least one embodiment, several node C.R.s716including CPUs, GPUs, DPUs, and/or other processors may be grouped within one or more racks to provide compute resources to support one or more workloads. The one or more racks may also include any number of power modules, cooling modules, and/or network switches, in any combination.

The resource orchestrator712may configure or otherwise control one or more node C.R.s716(1)-716(N) and/or grouped computing resources714. In at least one embodiment, resource orchestrator712may include a software design infrastructure (SDI) management entity for the data center700. The resource orchestrator712may include hardware, software, or some combination thereof.

In at least one embodiment, as shown inFIG.7, framework layer720may include a job scheduler733, a configuration manager734, a resource manager736, and/or a distributed file system738. The framework layer720may include a framework to support software732of software layer730and/or one or more application(s)742of application layer740. The software732or application(s)742may respectively include web-based service software or applications, such as those provided by Amazon Web Services, Google Cloud and Microsoft Azure. The framework layer720may be, but is not limited to, a type of free and open-source software web application framework such as Apache Spark™ (hereinafter “Spark”) that may utilize distributed file system738for large-scale data processing (e.g., “big data”). In at least one embodiment, job scheduler733may include a Spark driver to facilitate scheduling of workloads supported by various layers of data center700. The configuration manager734may be capable of configuring different layers such as software layer730and framework layer720including Spark and distributed file system738for supporting large-scale data processing. The resource manager736may be capable of managing clustered or grouped computing resources mapped to or allocated for support of distributed file system738and job scheduler733. In at least one embodiment, clustered or grouped computing resources may include grouped computing resource714at data center infrastructure layer710. The resource manager736may coordinate with resource orchestrator712to manage these mapped or allocated computing resources.

In at least one embodiment, software732included in software layer730may include software used by at least portions of node C.R.s716(1)-716(N), grouped computing resources714, and/or distributed file system738of framework layer720. One or more types of software may include, but are not limited to, Internet web page search software, e-mail virus scan software, database software, and streaming video content software.

In at least one embodiment, application(s)742included in application layer740may include one or more types of applications used by at least portions of node C.R.s716(1)-716(N), grouped computing resources714, and/or distributed file system738of framework layer720. One or more types of applications may include, but are not limited to, any number of a genomics application, a cognitive compute, and a machine learning application, including training or inferencing software, machine learning framework software (e.g., PyTorch, TensorFlow, Caffe, etc.), and/or other machine learning applications used in conjunction with one or more embodiments.

In at least one embodiment, any of configuration manager734, resource manager736, and resource orchestrator712may implement any number and type of self-modifying actions based on any amount and type of data acquired in any technically feasible fashion. Self-modifying actions may relieve a data center operator of data center700from making possibly bad configuration decisions and possibly avoiding underutilized and/or poor performing portions of a data center.

The data center700may include tools, services, software or other resources to train one or more machine learning models or predict or infer information using one or more machine learning models according to one or more embodiments described herein. For example, a machine learning model(s) may be trained by calculating weight parameters according to a neural network architecture using software and/or computing resources described above with respect to the data center700. The machine learning model(s) may also, or instead, be refined to detect and/or resist adversarial attacks. In at least one embodiment, trained or deployed machine learning models corresponding to one or more neural networks may be used to infer or predict information using resources described above with respect to the data center700by using weight parameters calculated through one or more training techniques, such as but not limited to those described herein.

Example Network Environments

Network environments suitable for use in implementing embodiments of the disclosure may include one or more client devices, servers, network attached storage (NAS), other backend devices, and/or other device types. The client devices, servers, and/or other device types (e.g., each device) may be implemented on one or more instances of the computing device(s)600ofFIG.6—e.g., each device may include similar components, features, and/or functionality of the computing device(s)600. In addition, where backend devices (e.g., servers, NAS, etc.) are implemented, the backend devices may be included as part of a data center700, an example of which is described in more detail herein with respect toFIG.7.

In sum, the disclosed techniques perform fine-tuning of a pre-trained machine learning model using adversarial images (or other types of adversarial data). The adversarial images can be generated by perturbing a set of original images based on one or more types of adversarial attack techniques. Once generated, the adversarial images are inputted into the pre-trained machine learning model, and the predictions generated by the pre-trained machine learning model from the inputted adversarial images are compared to labels for the original images from which the adversarial images were generated. Based on these comparisons, the adversarial images are divided into a “clean” dataset of adversarial images for which the pre-trained machine learning model generated correct predictions and an “adversarial” dataset of adversarial images for which the pre-trained machine learning model generated incorrect predictions.

The machine learning model is then refined using each dataset of adversarial images and a corresponding set of labels. During the refinement process, adversarial images in the clean dataset are inputted into the machine learning model, and the machine learning model is trained based on losses between predictions generated by the machine learning model from the inputted adversarial images and labels for original images that correspond to the inputted adversarial images.

Adversarial images in the adversarial dataset are also inputted into the machine learning model, and the machine learning model is trained based on losses between predictions generated by the machine learning model from the inputted adversarial images and labels that are determined based on perturbation strengths associated with the inputted adversarial images. More specifically, adversarial images that are in the adversarial dataset and associated with perturbation strengths of less than a threshold amount are assigned labels for the corresponding original images, and adversarial images that are in the adversarial dataset and associated with perturbation strengths of greater than the threshold amount are assigned an “adversarial label” that indicates that the corresponding images (or regions of images) are to be disregarded when determining actions to be performed based on the output of the machine learning model. The machine learning model is then trained based on losses between predictions generated by the machine learning model from the inputted adversarial images and the corresponding labels.

After the machine learning model has been trained using the adversarial images and corresponding labels, the machine learning model can be used in a visual ADS and/or another type of system to generate predictions for additional images. For example, the machine learning model may be used to detect and/or track objects in the vicinity of a self-driving car during operation of the self-driving car. The output of the machine learning model may be provided to one or more downstream components that generate commands and/or signals that are used to operate the self-driving car.

One technical advantage of the disclosed technique relative to the prior art is that a machine learning model is able to detect and correct for adversarial data that attempts to cause the machine learning model to operate incorrectly. Accordingly, a system that utilizes the output of the machine learning model is safer and more resistant to adversarial attacks than conventional systems that include machine learning models that have not been trained to recognize and/or defend against adversarial attacks. Another technical advantage of the disclosed techniques is a reduction in computational overhead and resource consumption, compared with prior art approaches that use an ensemble of multiple machine learning models to guard against adversarial attacks. These technical advantages provide one or more technological improvements over prior art approaches.

1. In some embodiments, a method comprises generating, using a machine learning model and based at least on a sensor data instance, one or more base outputs and one or more adversarial outputs that represent a likelihood that the sensor data instance is adversarial; determining that the sensor data instance is adversarial based on the one or more adversarial outputs; and sending, to one or more downstream components, an indication that the sensor data instance is adversarial to cause the one or more downstream components to perform one or more operations with respect to the one or more base outputs and in view of the indication.

2. The method of clause 1, further comprising generating, using the machine learning model and based at least on a second sensor data instance, one or more second base outputs and one or more second adversarial outputs that represent a likelihood that the second sensor data instance is adversarial: determining, using the one or more second base outputs, an output type corresponding to the second sensor data instance based at least on the one or more second adversarial outputs; and sending a second indication of the output type corresponding to the second sensor data instance to the one or more downstream components.

3. The method of any of clauses 1-2, wherein the determining the output type comprises matching a highest confidence included in the one or more second base outputs and the one or more second adversarial outputs to the output type.

4. The method of any of clauses 1-3, wherein the machine learning model was trained based at least on updating one or more parameters of the machine learning model based at least on one or more losses associated with a set of adversarial sensor data instances processed using the machine learning model.

5. The method of any of clauses 1-4, wherein the one or more losses are computed using a set of outputs generated using the machine learning model from the set of adversarial sensor data instances and a set of labels corresponding to the set of adversarial sensor data instances, wherein the set of labels comprises (1) an original label for an original sensor data instance that has been perturbed to produce a first adversarial sensor data instance included in the set of adversarial sensor data instances, and (2) an adversarial label indicating that a second adversarial sensor data instance included in the set of adversarial sensor data instances corresponds to an adversarial attack.

6. The method of any of clauses 1-5, wherein the determining that the sensor data instance is adversarial comprises determining that at least one of the one or more adversarial outputs exceeds a threshold value.

7. The method of any of clauses 1-6, wherein the determining that the sensor data instance is adversarial comprises determining that the one or more adversarial outputs include confidences that are higher than confidences associated with the one or more base outputs.

8. The method of any of clauses 1-7, wherein the sensor data instance comprises one or more images.

9. The method of any of clauses 1-8, wherein the one or more base outputs correspond to a set of classes associated with objects.

10. The method of any of clauses 1-9, wherein the one or more downstream components are included in at least one of a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing deep learning operations; a system implemented using an edge device; a system for generating or presenting at least one of virtual reality content, augmented reality content, or mixed reality content; a system implemented using a robot; a system for performing conversational AI operations; a system for generating synthetic data; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources.

11. In some embodiments, a method comprises generating, using a machine learning model and based at least on a perturbed sensor data instance, an output that includes a base output and an adversarial output, the adversarial output corresponding to a likelihood that the perturbed sensor data instance is adversarial; computing one or more loss values based at least on the base output, the adversarial output, and ground truth data indicating that the perturbed sensor data instance is adversarial; and updating one or more parameters of the machine learning model based at least on the one or more loss values.

12. The method of clause 11, further comprising generating, using the machine learning model and based at least on a second perturbed sensor data instance, a second base output and a second adversarial output, the second adversarial output corresponding to a likelihood that the second perturbed sensor data instance is adversarial: computing one or more second loss values based at least on the second base output, the second adversarial output, and second ground truth data corresponding to an original sensor data instance that was perturbed to generate the second perturbed sensor data instance; and updating at least one of the one or more parameters or one or more other parameters of the machine learning model based at least on the one or more second loss values.

13. The method of any of clauses 11-12, further comprising assigning the second ground truth data to the second perturbed sensor data instance based at least on a match between the second ground truth data and a previous output generated using the machine learning model based at least on the second perturbed sensor data instance.

14. The method of any of clauses 11-13, further comprising determining the second ground truth data based at least on a perturbation strength associated with the second perturbed sensor data instance falling below a threshold perturbation strength.

15. The method of any of clauses 11-14, wherein the computing the one or more second loss values is further based at least on the second base output and third ground truth data that indicates that the second perturbed sensor data instance corresponds to an adversarial attack, wherein the second ground truth data comprises a first target probability for an output type of the second base output and the third ground truth data comprises a second target probability of the second adversarial output.

16. The method of any of clauses 11-15, further comprising determining the ground truth data based at least on a perturbation strength associated with the perturbed sensor data instance exceeding a threshold perturbation strength.

17. The method of any of clauses 11-16, further comprising applying one or more perturbations to an original sensor data instance to generate the perturbed sensor data instance.

18. The method of any of clauses 11-17, wherein the one or more perturbations comprise at least one of a fast gradient step method (FGSM), a projected gradient descent (PGD) technique, a limited-memory Broyden-Fletcher-Goldfarm-Shannon (L-BGFS) technique, a Carlini & Wagner technique, a Jacobian-based Saliance Map Attack (JSMA), or a DeepFool technique.

19. In some embodiments, a system comprises one or more processing units to perform, based at least on an indication of an adversarial attack, one or more operations with respect to a base output of a neural network, the indication of the adversarial attack generated based at least on an adversarial output of the neural network different than the base output.

20. The system of claim19, wherein the system is comprised in at least one of a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine: a system for performing simulation operations; a system for performing digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing deep learning operations; a system implemented using an edge device; a system for generating or presenting at least one of virtual reality content, augmented reality content, or mixed reality content; a system implemented using a robot; a system for performing conversational AI operations: a system for generating synthetic data; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources.