SYSTEM AND METHOD FOR OVERCOMING REAL-WORLD LOSSES IN MACHINE LEARNING APPLICATIONS

In an approach to integrating real-world properties into machine learning training, a real-world image is received. The real-world image is compared to a simulated image, where the comparison is performed using a discriminator network of a generative adversarial network (GAN). A generator network of the GAN is trained with results of the comparison of the real-world image to the simulated image. Responsive to determining that the real-world image is not optimal, the real-world image is iteratively tuned, using the generator network of the GAN, until it is determined that the real-world image is optimal, where the real-world image is optimal if the real-world image meets a predetermined threshold for accuracy of one or more image parameters of the simulated image versus the real-world image. The discriminator network of the GAN is trained with the real-world image.

FIELD

The present application relates generally to machine learning and, more particularly, to overcoming real-world losses in machine learning applications.

BACKGROUND

Artificial intelligence (AI) can be defined as the theory and development of computer systems able to perform tasks that normally require human intelligence, such as speech recognition, visual perception, decision-making, and translation between languages. The term AI is often used to describe systems that mimic cognitive functions of the human mind, such as learning and problem solving. Machine learning (ML) is an application of AI that creates systems that have the ability to automatically learn and improve from experience.

Adversarial learning, a subfield to machine learning, generates patterns that can be viewed by a trained machine learning model to induce an irregular response. It is necessary to understand the vulnerabilities of machine learning models so that cybersecurity professionals can mitigate attacks against real systems. It is pertinent to understand the ramifications of these responses, and thus necessary to create and test these attacks against models. Testing attacks that could occur in the real world requires accurate display of these patterns in comparison to the trained model (e.g., similar attributes such as color and brightness).

DETAILED DESCRIPTION

The present disclosure is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the drawings. The examples described herein may be capable of other embodiments and of being practiced or being carried out in various ways. Also, it may be appreciated that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting as such may be understood by one of skill in the art. Throughout the present description, like reference characters may indicate like structure throughout the several views, and such structure need not be separately discussed. Furthermore, any particular feature(s) of a particular exemplary embodiment may be equally applied to any other exemplary embodiment(s) of this specification as suitable. In other words, features between the various exemplary embodiments described herein are interchangeable, and not exclusive.

Machine learning (ML) is an application of AI that creates systems that have the ability to automatically learn and improve from experience. ML involves the development of computer programs that can access data and learn based on that data. ML algorithms typically build mathematical models based on sample, or training, data in order to make predictions or decisions without being explicitly programmed to do so. The use of training data in ML requires human intervention for feature extraction in creating the training data set. The two main types of ML are Supervised learning and Unsupervised learning. Supervised learning uses labeled datasets that are designed to train or “supervise” algorithms into classifying data or predicting outcomes accurately. Supervised learning is typically used for problems requiring classification or regression analysis. Classification problems use an algorithm to accurately assign test data into specific categories. Regression is a method that uses an algorithm to understand the relationship between dependent and independent variables. Regression models are helpful for predicting numerical values based on different data points. Unsupervised learning uses machine learning algorithms to analyze and cluster unlabeled datasets. These algorithms discover hidden patterns or data groupings without the need for human intervention, and their ability to discover similarities and differences in information make unsupervised learning the ideal solution for exploratory data analysis, cross-selling strategies, customer segmentation, and image recognition.

Adversarial Learning, a subfield to machine learning, generates patterns that can be viewed by a trained machine learning model to induce an irregular response. It is necessary to understand the vulnerabilities of machine learning models so that, for example, cybersecurity professionals can mitigate attacks against real systems. In another example, the irregular response may be a misidentification of a person or an object. It is pertinent to understand the ramifications of these responses, and thus necessary to create and test these attacks against models. Testing attacks that could occur in the real world requires accurate display of these patterns in comparison to the trained model (e.g., similar attributes such as color and brightness).

A generative adversarial network (GAN) is a class of machine learning where two neural networks contest with each other. The GAN is comprised of generator and discriminator networks. The generator learns to generate plausible data. The generated instances become negative training examples for the discriminator. The discriminator in a GAN is typically a classifier that learns to distinguish the generator's fake data from real data. Given a training set, this technique learns to generate new data with the same statistics as the training set. For example, a GAN trained on photographs can generate new photographs that look at least superficially authentic to human observers, having many realistic characteristics. The core idea of a GAN is based on the “indirect” training through the discriminator, another neural network that is able to tell how much an input is “realistic”, which itself is also being updated dynamically. This basically means that the generator is not trained to minimize the distance to a specific image, but rather to fool the discriminator. This enables the model to learn in an unsupervised manner.

Currently, most machine learning models are trained on pre-captured data, and for adversarial learning, a digital pattern is overlayed onto the image which is then “trained” repeatedly. However, several issues become apparent once this digital pattern is printed and tested in front of a real camera, including uncontrollable environmental lighting and subpar printer quality used to print out the pattern. These issues compound and become difficult to accurately model during training, and there are no straightforward objective functions which help to solve these issues. These losses can be visualized in the example ofFIG.2below.

For these patterns to exploit object detection neural networks in a real-world environment, they require accurate color representation. One problem is that due to losses during printing (e.g., imperfect color representation when transitioning from typical screen red green blue [RGB] color space to typical printer cyan magenta yellow black [CMYK] color space), losses during display on an electronic display device, e.g., a liquid crystal display (LCD), a light-emitting diode (LED) display, or an electrophoretic, i.e., e-ink, display, and losses during live capture (e.g., glare from the sun on the glossy poster material), it is too complex to model and account for these losses in a simulated training environment.

In some embodiments, different GAN architectures may be used to generate a synthetic pattern which would then be displayed on an image display device. An image capture device captures another image (now with the display showing the synthetic pattern), and the generator network extracts several fake subsamples. The discriminator network is then fed both the real and fake subsamples. Depending on which architecture is used, the losses are used to generate the gradients for backpropagation, which train the weights of the neural networks.

In some embodiments, the GAN is a modified version of the Boundary Equilibrium GAN (BEGAN). This architecture forces the generator and discriminator networks into equilibrium and balances them during training using a derivation of the Wasserstein Distance. This architecture is unique because it uses autoencoders for both the generator and discriminator networks, and measures the loss based on the distributions generated by both.

In some other embodiments, the GAN architecture is a modified version of the Wasserstein GAN with Gradient Penalty (WGAN-GP). This architecture uses a ratio, ncritic, which is a hyperparameter that specifies how many iterations the discriminator network should perform gradient descent for each time the generator network performs gradient descent. The rationale is that typically the task of the discriminator network is more difficult than the task of the generator network, and thus the former typically requires more optimization than the latter.

FIG.2is an example of a simulated adversarial image versus a real-world adversarial image. Simulated adversarial image202is an example of an image as generated by an adversarial learning system. Real-world adversarial image204is an example of the simulated adversarial image202when it is displayed on a real-world device, e.g., a display output or a printed image. Disclosed herein is a system and a computer-implemented method for overcoming real-world losses in machine learning applications by training neural networks to generate optimal patterns by directly training in the real-world environment in which it will be tested, integrating the real-world properties of displays, environments, sensors, etc.

Another example of a simulated adversarial image versus a real-world adversarial image is in adaptive camouflage. Adversarial learning software can use a wide range of colors and patterns to generate realistic camouflage, but it requires knowing exactly what to account for (e.g., light sources, printer losses) to be successful. The disclosed system and computer-implemented method for overcoming real-world losses in machine learning applications may be used as an end-to-end training process for creating and tuning camouflage patterns. This method greatly increases the effectiveness of the pattern in real-world environments by optimizing over all the losses that one would typically have when using conventional training methods. While GANs have been used to mask and mimic individuals in video recordings, this is the first time GANs have been used to develop optimized camouflage for a given background environment. In addition, the disclosed system and method is able to implement the end-to-end training method on both color Electronic electrophoretic displays as well as LED monitor displays.

FIG.3is an example overview of the process for overcoming real-world losses in machine learning applications consistent with the present disclosure. The example ofFIG.3shows only one image display device, image display device302, although in other embodiments any number of image display device may be used. In some embodiments, image display device302may be a display such as an LED display, an LCD display, or an electrophoretic display. In some other embodiments, image display device302may be a printed image, or the printer used to generate the printed image.

The example ofFIG.3shows only one image capture device, image capture device304, although in other embodiments any number of image capture devices may be used. Image capture device304captures an image displayed by image display device302and sends the image to computer306. Computer306uses the image received from image capture device304to train a GAN comprised of generator and discriminator networks. The generator network receives an image from the image capture device and randomly subsamples it to get pieces of the overall environment. It then generates a pattern, which is sent on to the updateable display. Once the display has been updated, the discriminator network then receives another image from the image capture device and randomly chooses subsamples from the new image that contains a pre-determined pixel amount of the display. The general task of the discriminator network is to determine which subsamples contain only the real-world environment (real images) and which subsamples contain some (or all) of the pattern display (fake images). The task of the discriminator network is to continuously improve its ability to discriminate between fake and real images, while the task of the generator network is to continuously improve its ability to fool the discriminator network.

FIG.1is a functional block diagram illustrating a distributed data processing environment, generally designated100, suitable for operation of program112, consistent with the present disclosure. The term “distributed” as used herein describes a computer system that includes multiple, physically distinct devices that operate together as a single computer system.FIG.1provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environment may be made by those skilled in the art without departing from the scope of the disclosure as recited by the claims.

Distributed data processing environment100includes computing device110optionally connected to network130. Network130can be, for example, a telecommunications network, a Local Area Network (LAN), a Wide Area Network (WAN), such as the Internet, or a combination of the three, and can include wired, wireless, or fiber optic connections. Network130can include one or more wired and/or wireless networks that are capable of receiving and transmitting data, voice, and/or video signals, including multimedia signals that include voice, data, and video information. In general, network130can be any combination of connections and protocols that will support communications between computing device110and other computing devices (not shown) within distributed data processing environment100.

Computing device110can be a standalone computing device, a management server, a web server, a mobile computing device, or any other electronic device or computing system capable of receiving, sending, and processing data. In an embodiment, computing device110can be a laptop computer, a Personal Computer (PC), a desktop computer, or any programmable electronic device capable of communicating with other computing devices (not shown) within distributed data processing environment100via network130. In another embodiment, computing device110can represent a server computing system utilizing multiple computers as a server system, such as in a cloud computing environment. In yet another embodiment, computing device110represents a computing system utilizing clustered computers and components (e.g., database server computers, application server computers) that act as a single pool of seamless resources when accessed within distributed data processing environment100.

In some embodiments, computing device110includes program112. In an embodiment, program112is a program, application, or subprogram of a larger program for overcoming real-world losses in machine learning applications. In an alternative embodiment, program112may be located on any other device accessible by computing device110via network130.

In some embodiments, computing device110includes information repository114. In an embodiment, information repository114may be managed by program112. In an alternate embodiment, information repository114may be managed by the operating system of the computing device110, alone, or together with, program112. Information repository114is a data repository that can store, gather, compare, and/or combine information. In some embodiments, information repository114is located externally to computing device110and accessed through a communication network, such as network130. In some embodiments, information repository114is stored on computing device110. In some embodiments, information repository114may reside on another computing device (not shown), provided that information repository114is accessible by computing device110. Information repository114includes, but is not limited to, AI data, learning data, process data, and other data that is received by program112from one or more sources, and data that is created by program112.

Information repository114may be implemented using any non-transitory volatile or non-volatile storage media for storing information, as known in the art. For example, information repository114may be implemented with Random-Access Memory (RAM), Solid-State Drives (SSD), one or more independent hard disk drives, multiple hard disk drives in a Redundant Array of Independent Disks (RAID), optical library, or a tape library. Similarly, information repository114may be implemented with any suitable storage architecture known in the art, such as a relational database, an object-oriented database, or one or more tables.

In some embodiments, distributed data processing environment100includes one or more image capture devices, e.g., image capture device120ofFIG.1. Image capture device may be a still camera, a video camera, or any other device capable of capturing an image and transmitting the image to computing device110. In some embodiments, image capture device120is directly connected to computing device110. In some other embodiments, image capture device120connects to computing device110through network130.

FIG.4is a flowchart diagram depicting operations for the program112for overcoming real-world losses in machine learning applications, on the distributed data processing environment ofFIG.1, consistent with the present disclosure. In an alternative embodiment, the operations of workflow400may be performed by any other program while working with the program112.

It should be appreciated that embodiments of the present disclosure provide at least for overcoming real-world losses in machine learning applications. However,FIG.4provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environment may be made by those skilled in the art without departing from the scope of the disclosure as recited by the claims.

The program112outputs an image on the image display device (operation402). In one example embodiment, the program112outputs a simulated adversarial image on the image display device, e.g., the image display device302fromFIG.3. In some embodiments, the image display device may be an LCD, LED, or electrophoretic display. In some other embodiments, the image display device may be a hard copy image, such as an image printed on a printer device.

The program112captures the image from the image display device (operation404). In operation403B, the program112uses an image capture device, e.g., image capture device120fromFIG.1, to capture a real-world adversarial image of the simulated adversarial image on the image display device.

The program112determines if the captured image is optimal (decision block406). The program112determines if the captured image is optimal, i.e., if the captured image meets a predetermined threshold for accuracy of one or more image parameters of the simulated adversarial image versus the real-world adversarial image, as described above.

In some embodiments, the image display device is a computer monitor. In these embodiments, the one or more image parameters may include, but are not limited to, brightness (or luminance), color gamut (a range of colors within the spectrum of colors, or a color space, that can be reproduced on an output device), resolution, contrast, color temperature, and viewing direction. In some other embodiments, the image display device may be a printed image. In these embodiments, the one or more image parameters may include, but is not limited to, color space, i.e., the conversion from the RGB (red, green, and blue) color space of a display versus the CMYK (cyan, magenta, yellow, and black) color space of a printer. Generally, the CMYK color space of a printer is very small compared to the RGB color space of a color monitor. When the real-world adversarial image is a printed image, matching it to the desired simulated adversarial image is difficult.

In order to determine if the captured image is optimal, one or more image parameters and predetermined thresholds for these image parameters are chosen in advance. When the real-world adversarial image is received, the program112will use the discriminator network of the GAN to determine if the one or more image parameters are within the predetermined thresholds. If the discriminator network determines that the parameters are within the predetermined thresholds, then the image is considered real, and the process is complete. If the discriminator network determines that the parameters are not within the predetermined thresholds, then the image is considered fake, and the process continues to tune the image.

If the program112determines that the captured image is optimal (“yes” branch, decision block406), then the program112ends for this cycle. If the program112determines that the captured image is not optimal (“no” branch, decision block406), then the program112proceeds to operation408to update the GAN models.

The program112trains the GAN models with the tuned parameters (operation408). In operation408, the program112trains the generator network with the output of the discriminator network and trains the discriminator network with the output of the generator network, thereby updating the GAN models with updated image parameters to use in tuning the real-world image in operation410.

The program112tunes the image (operation410). In operation410, the program112uses the generator network of the GAN to tune the real-world adversarial image based on the results from the discriminator network of the GAN from decision block406. The program112then generates a tuned image to display on the image display device. The program112then returns to operation402to output the tuned image on the image display device.

FIG.5is a block diagram depicting components of one example500of the computing device102suitable for program112, within the distributed data processing environment ofFIG.1, consistent with the present disclosure.FIG.5displays the computing device or computer500, one or more processor(s)504(including one or more computer processors), a communications fabric502, a memory506including, a random-access memory (RAM)516and a cache518, a persistent storage508, a communications unit512, I/O interfaces514, a display522, and external devices520. It should be appreciated thatFIG.5provides only an illustration of one embodiment and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environment may be made.

As depicted, the computer500operates over the communications fabric502, which provides communications between the computer processor(s)504, memory506, persistent storage508, communications unit512, and input/output (I/O) interface(s)514. The communications fabric502may be implemented with an architecture suitable for passing data or control information between the processors504(e.g., microprocessors, communications processors, and network processors), the memory506, the external devices520, and any other hardware components within a system. For example, the communications fabric502may be implemented with one or more buses.

The memory506and persistent storage508are computer readable storage media. In the depicted embodiment, the memory506comprises a RAM516and a cache518. In general, the memory506can include any suitable volatile or non-volatile computer readable storage media. Cache518is a fast memory that enhances the performance of processor(s)504by holding recently accessed data, and near recently accessed data, from RAM516.

Program instructions for program112may be stored in the persistent storage508, or more generally, any computer readable storage media, for execution by one or more of the respective computer processors504via one or more memories of the memory506. The persistent storage508may be a magnetic hard disk drive, a solid-state disk drive, a semiconductor storage device, flash memory, read only memory (ROM), electronically erasable programmable read-only memory (EEPROM), or any other computer readable storage media that is capable of storing program instruction or digital information.

The communications unit512, in these examples, provides for communications with other data processing systems or devices. In these examples, the communications unit512includes one or more network interface cards. The communications unit512may provide communications through the use of either or both physical and wireless communications links. In the context of some embodiments of the present disclosure, the source of the various input data may be physically remote to the computer500such that the input data may be received, and the output similarly transmitted via the communications unit512.

The I/O interface(s)514allows for input and output of data with other devices that may be connected to computer500. For example, the I/O interface(s)514may provide a connection to external device(s)520such as a keyboard, a keypad, a touch screen, a microphone, a digital camera, and/or some other suitable input device. External device(s)520can also include portable computer readable storage media such as, for example, thumb drives, portable optical or magnetic disks, and memory cards. Software and data used to practice embodiments of the present disclosure, e.g., program112, can be stored on such portable computer readable storage media and can be loaded onto persistent storage508via the I/O interface(s)514. I/O interface(s)514also connect to a display522.

Display522provides a mechanism to display data to a user and may be, for example, a computer monitor. Display522can also function as a touchscreen, such as a display of a tablet computer.

According to one aspect of the disclosure, there is provided a computer-implemented method for the detection of malicious activity. The computer-implemented method includes: receiving a real-world image; comparing the real-world image to a simulated image, wherein comparing the real-world image to the simulated image is performed using a discriminator network of a generative adversarial network (GAN); training a generator network of the GAN with results of comparing the real-world image to the simulated image; responsive to determining that the real-world image is not optimal, iteratively tuning the real-world image, using the generator network of the GAN, until determining that the real-world image is optimal, wherein the real-world image is optimal if the real-world image meets a predetermined threshold for accuracy of one or more image parameters of the simulated image versus the real-world image; and training the discriminator network of the GAN with the real-world image.

According to another aspect of the disclosure, there is provided a system for the detection of malicious activity. The system includes: one or more computer processors; one or more computer readable storage media; and program instructions stored on the one or more computer readable storage media for execution by at least one of the one or more computer processors, the stored program instructions including instructions to: receive a real-world image; compare the real-world image to a simulated image, wherein comparing the real-world image to the simulated image is performed using a discriminator network of a generative adversarial network (GAN); train a generator network of the GAN with results of comparing the real-world image to the simulated image; responsive to determining that the real-world image is not optimal, iteratively tune the real-world image, using the generator network of the GAN, until determining that the real-world image is optimal, wherein the real-world image is optimal if the real-world image meets a predetermined threshold for accuracy of one or more image parameters of the simulated image versus the real-world image; and train the discriminator network of the GAN with the real-world image.

According to yet another aspect of the disclosure, there is provided a system for the detection of malicious activity. The system includes: one or more image display devices; one or more image capture devices; one or more computer processors; one or more computer readable storage media; and program instructions stored on the one or more computer readable storage media for execution by at least one of the one or more computer processors, the stored program instructions including instructions to: capture a simulated image from any of the one or more image display devices using any of the one or more image capture devices; receive a real-world image; compare the real-world image to the simulated image, wherein comparing the real-world image to the simulated image is performed using a discriminator network of a generative adversarial network (GAN); train a generator network of the GAN with results of comparing the real-world image to the simulated image; responsive to determining that the real-world image is not optimal, iteratively tune the real-world image, using the generator network of the GAN, until determining that the real-world image is optimal, wherein the real-world image is optimal if the real-world image meets a predetermined threshold for accuracy of one or more image parameters of the simulated image versus the real-world image; and train the discriminator network of the GAN with the real-world image.

The programs described herein are identified based upon the application for which they are implemented in a specific embodiment of the disclosure. However, it should be appreciated that any particular program nomenclature herein is used merely for convenience, and thus the disclosure should not be limited to use solely in any specific application identified and/or implied by such nomenclature.