Patent Publication Number: US-11657153-B2

Title: System and method for detecting an adversarial attack

Description:
FIELD 
     This disclosure relates generally to machine learning systems, and more particularly to detecting a sequence of adversarial data. 
     BACKGROUND 
     In general, machine learning systems, particularly deep neural networks, are susceptible to adversarial attacks. These adversarial attacks may include black box attacks, which relate to attacks based on knowledge of the expected output of the machine learning system, and/or white-box attacks, which relate to attacks based on knowledge of the internal workings of the machine learning system. As an example, a machine learning system may be attacked via its input. Such adversarial attacks find perturbations on the inputs that cause changes to the output data of the machine learning system. These adversarial attacks are typically performed by updating the perturbations on the input data based on feedback until the machine learning system makes determinations that are corrupted by these perturbations such that incorrect output data (e.g., misclassifications of input data) is generated, thereby resulting in negative consequences and effects. 
     SUMMARY 
     The following is a summary of certain embodiments described in detail below. The described aspects are presented merely to provide the reader with a brief summary of these certain embodiments and the description of these aspects is not intended to limit the scope of this disclosure. Indeed, this disclosure may encompass a variety of aspects that may not be explicitly set forth below. 
     According to at least one aspect, a computer-implemented method relates to training a machine learning system to detect an adversarial attack. The method includes obtaining a collection of sequences. The collection of sequences include at least a first sequence and a second sequence. The method includes classifying the first sequence as belonging to a first class indicative of a nominal sequence based on a first prediction that the first sequence includes an unperturbed version of sensor data. The method includes classifying the second sequence as belonging to a second class indicative of an adversarial sequence based on a second prediction that the second sequence includes a perturbed version of the sensor data. The method includes generating combined loss data based on (i) a first average loss involving incorrect classifications of the first class with respect to a first set of sequences from the collection of sequences in which each sequence within the first set of sequences is the nominal sequence and (ii) a second average loss involving incorrect classifications of the second class with respect to a second set of sequences from the collection of sequences in which each sequence within the second set of sequences is the adversarial sequence. The method includes updating parameters of the machine learning system based on the combined loss data. 
     According to at least one aspect, a non-transitory computer readable medium comprises computer readable data which, when executed by a processor, causes the processor to perform a method. The method includes obtaining a collection of sequences. The collection of sequences include at least a first sequence and a second sequence. The method includes classifying the first sequence as belonging to a first class indicative of a nominal sequence based on a first prediction that the first sequence includes an unperturbed version of sensor data. The method includes classifying the second sequence as belonging to a second class indicative of an adversarial sequence based on a second prediction that the second sequence includes a perturbed version of the sensor data. The method includes generating combined loss data based on (i) a first average loss involving incorrect classifications of the first class with respect to a first set of sequences from the collection of sequences in which each sequence within the first set of sequences is the nominal sequence and (ii) a second average loss involving incorrect classifications of the second class with respect to a second set of sequences from the collection of sequences in which each sequence within the second set of sequences is the adversarial sequence. The method includes updating parameters of the machine learning system based on the combined loss data. 
     According to at least one aspect, a computer-implemented method relates to defending against an adversarial attack. The method includes obtaining a sequence of inputs to a first machine learning system. The method includes generating an adversarial label to classify the sequence of inputs as being adversarial based on a statistical determination that the sequence of inputs is a perturbed version of q plurality of frames of sensor data. The method includes identifying a sequence of output data that is generated by the first machine learning system based on the sequence of inputs. The method includes filtering out the sequence of output data based on the adversarial label to prevent an actuator system from being controlled based on the sequence of output data. 
     These and other features, aspects, and advantages of the present invention are discussed in the following detailed description in accordance with the accompanying drawings throughout which like characters represent similar or like parts. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a diagram of an example of a system that includes a detector and an adversarial defense system according to an example embodiment of this disclosure. 
         FIG.  2    is a diagram of an example of the system of  FIG.  1    with respect to mobile machine technology according to an example embodiment of this disclosure. 
         FIG.  3 A  is a conceptual diagram of some components of the system of  FIG.  1    with respect to a nominal mode of operation according to an example embodiment of this disclosure. 
         FIG.  3 B  is a conceptual diagram of some components of the system of  FIG.  1    with respect to a defensive mode of operation according to an example embodiment of this disclosure. 
         FIG.  4    is a diagram of an example of a system associated with training a detector according to an example embodiment of this disclosure. 
         FIG.  5    is a flow diagram associated with training a detector according to an example embodiment of this disclosure. 
         FIG.  6 A  is a conceptual diagram of examples of adversarial sequences that are generated based on a nominal sequence according to an example embodiment of this disclosure. 
         FIG.  6 B  is a conceptual diagram of other examples of adversarial sequences that are generated based on a nominal sequence according to an example embodiment of this disclosure. 
         FIG.  7    is a flow diagram of an example of a training process for generating the detector according to an example embodiment of this disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     The embodiments described herein, which have been shown and described by way of example, and many of their advantages will be understood by the foregoing description, and it will be apparent that various changes can be made in the form, construction, and arrangement of the components without departing from the disclosed subject matter or without sacrificing one or more of its advantages. Indeed, the described forms of these embodiments are merely explanatory. These embodiments are susceptible to various modifications and alternative forms, and the following claims are intended to encompass and include such changes and not be limited to the particular forms disclosed, but rather to cover all modifications, equivalents, and alternatives falling with the spirit and scope of this disclosure. 
       FIG.  1    is a diagram of a system  100  that includes at a sensor system  110 , a control system  120 , and an actuator system  130 . The system  100  is configured such that the control system  120  controls the actuator system  130  based on sensor data from the sensor system  110 . More specifically, the sensor system  110  includes one or more sensors and/or corresponding devices to generate sensor data. For example, the sensor system  110  includes an image sensor, a camera, a radar sensor, a light detection and ranging (LIDAR) sensor, a thermal sensor, an ultrasonic sensor, an infrared sensor, a motion sensor, a satellite-based navigation sensor (e.g., Global Positioning System (GPS) sensor), a microphone, any suitable sensor, or any combination thereof. Upon obtaining detections of its environment, the sensor system  110  is operable to communicate with the control system  120  via an input/output (I/O) system  140  and/or other functional modules  150 , which includes communication technology. 
     The control system  120  is configured to obtain the sensor data directly or indirectly from one or more sensors of the sensor system  110 . In this regard, the sensor data may include sensor data from a single sensor or sensor-fusion data from a plurality of sensors. Upon receiving input, which includes at least sensor data, the control system  120  may implement a software mechanism, such as a sliding window, to obtain at least one sequence from a stream of sensor data. Each sequence may be any length and may include any number of elements. In an example, each sequence includes elements, where each element is a frame that includes at least sensor data. The control system is operable to process the sensor data via a processing system  160 . In this regard, the processing system  160  includes at least one processor. For example, the processing system  160  includes an electronic processor, a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor, a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), processing circuits, any suitable processing technology, or any combination thereof. Upon processing at least this sensor data, the processing system  160  is operable to generate output data based on communications with memory system  170 . In addition, the processing system  160  is operable to provide control data to the actuator system  130  based on the output data. 
     The memory system  170  is a computer or electronic storage system, which is configured to store and provide access to various data to enable at least the operations and functionality, as disclosed herein. The memory system  170  comprises a single device or a plurality of devices. The memory system  170  includes electrical, electronic, magnetic, optical, semiconductor, electromagnetic, any suitable memory technology, or any combination thereof. For instance, the memory system  170  may include random access memory (RAM), read only memory (ROM), flash memory, a disk drive, a memory card, an optical storage device, a magnetic storage device, a memory module, any suitable type of memory device, or any combination thereof. In an example embodiment, with respect to the control system  120  and/or processing system  150 , the memory system  170  is local, remote, or a combination thereof (e.g., partly local and partly remote). For example, the memory system  170  is configurable to include at least a cloud-based storage system (e.g. cloud-based database system), which is remote from the processing system  160  and/or other components of the control system  120 . 
     The memory system  170  includes at least a classifier  200 . The classifier  200  includes a machine learning system  200 A. The machine learning system  200 A includes at least one artificial neural network (e.g., deep neural network) or any suitable machine learning technology. For convenience of reference, this machine learning system  200 A is sometimes referred to as the “first machine learning system” in this disclosure. In response to input, the processing system  160 , via the machine learning system  200 A, is operable to generate output data based on the input. For example, upon receiving at least sensor data from the sensor system  110 , the processing system  160 , via application of the machine learning system  200 A, is operable to predict a class for an entity of the sensor data and provide class data as output data. As a non-limiting example, for instance, the processing system  160 , via the machine learning system  200 A, is configured to assign a “pedestrian” class from among a plurality of classes (e.g., traffic sign, traffic light, animal, vehicle, etc.) upon determining that a detected entity of the sensor data most likely belongs to the pedestrian class. Upon generating this output data (e.g., class data indicating a pedestrian) via the machine learning system  200 A, the processing system  160  is operable to generate control data for the actuator system  130  based at least on this output data (e.g., “pedestrian”) such that the actuator system  130  is controlled to perform an action that takes into account the detections of the sensor system  110 . 
     The memory system  170  also includes a detector  210 . The detector  210  is advantageously configured to discriminate between nominal sequences and adversarial sequences. More specifically, the detector  210  includes at least one machine-learning system  210 A. For convenience of reference, this machine learning system  210 A is sometimes referred to as the “second machine learning system” to differentiate it from the machine learning system  200 A, which may be referred to as the “first machine learning system.” More specifically, the detector  210  includes at least one neural network and/or deep neural network architecture, which is tailored for temporal sequences. For example, the detector  210  includes at least a recursive neural network (RNN), a long short-term memory (LSTM) network, a gated recursive unit (GRU), other suitable machine learning technology, or any combination thereof. 
     The detector  210 , via the processing system  160 , is configured to obtain the same inputs that are directly received and processed by the classifier  200 . More specifically, the machine learning system  210 A of the detector  210 , via the processing system  160 , obtains a sequence of inputs at the same time or within a similar timeframe as the machine learning system  200  of the classifier  200 . The detector  210  may be at least partly integral with the machine learning system  200 A and/or at least partly separate from the machine learning system  200 A. Upon receiving a sequence of inputs, the processing system  160 , via the detector  210  is configured to generate a nominal label, indicative of a detection of a “nominal sequence,” upon statistically determining that the sequence of inputs includes a sequence of nominal data and generate an adversarial label, indicative of a detection of “an adversarial sequence,” upon statistically determining that the sequence of inputs includes a sequence of adversarial data or a sequence of non-nominal data. By evaluating sequences, the detector  210  is configured to determine an absence or presence of an adversarial attack to the machine learning system  200 A as adversarial attacks often involve iterative attempts at perturbing inputs in order to cause the machine learning system  200 A to fail. 
     The memory system  170  also includes an adversarial defense system  220 . The adversarial defense system  220  includes at least software technology. Additionally or alternatively, the adversarial defense system  220  may include hardware technology. The adversarial defense system  220  is configured to be, at least partly, separate from or integral with the detector  210 . The adversarial defense system  220 , via the processing system  160 , is configured to receive output data from the machine learning system  200 A and corresponding classification data from the detector  210 . More specifically, the adversarial defense system  220  receives the sequence of output data from the machine learning system  200 A that are generated based on the same sequence of inputs that the classification data classifies. In this regard, the processing system  160  is configured to identify the sequence of output data from the machine learning system  200 A that corresponds to the classification data for the sequence of inputs to the machine learning system  200 A via at least timestamp data or any suitable correlation data. 
     The adversarial defense system  220 , via the processing system  160 , is advantageously configured to ensure that at least one other system (e.g., actuator system  130 ) is protected from directly or indirectly receiving a sequence of output data from the machine learning system  200 A that has been generated based on a sequence of inputs that is deemed to be an adversarial sequence by the detector  210 . More specifically, upon receiving an adversarial label from the detector  210 , the adversarial defense system  220 , via the processing system  160 , is configured to take defensive action with respect to the identified sequence of output data from the machine learning system  200 A that corresponds to the adversarial sequence. For example, the adversarial defense system  220  is configured to delay the output data, replace the output data with predetermined output data (e.g., default output data, an alert, etc.), reject the output data, filter out the output data, discard the output data, and/or take any suitable action that prevents the system  100  from acting on the sequence of output data that is generated based on the detected sequence of adversarial data. Accordingly, the adversarial defense system  220 , when cooperating with the detector  210  and the processing system  160 , is configured ensure that the system  100  operates with a predetermined level of accuracy by ensuring that the system  100  only acts upon a sequence of output data, which is generated based on a sequence of inputs that is deemed nominal. 
     Furthermore, as shown in  FIG.  1   , the system  100  includes other components that contribute to operation of the control system  120  in relation to the sensor system  110  and the actuator system  130 . For example, as shown in  FIG.  1   , the memory system  170  is also configured to store other relevant data  230 , which relates to the operation of the system  100  in relation to one or more components (e.g., sensor system  110 , the actuator system  130 , the machine learning system  200 A, the detector  210 , and the adversarial defense system  220 ). Also, as shown in  FIG.  1   , the control system  120  includes the I/O system  140 , which includes one or more interfaces for one or more I/O devices that relate to the system  100 . For example, the I/O system  140  provides at least one interface to the sensor system  110  and at least one interface to the actuator system  130 . Also, the control system  120  is configured to provide other functional modules  150 , such as any appropriate hardware, software, or any combination thereof that assist with and/or contribute to the functioning of the system  100 . For example, the other functional modules  150  include an operating system and communication technology that enables components of the system  100  to communicate with each other as described herein. With at least the configuration discussed in the example of  FIG.  1   , the system  100  is applicable in various technologies. 
       FIG.  2    is a diagram of an example of the system  100  with respect to mobile machine technology according to an example embodiment. More specifically, in  FIG.  2   , the system  100  is employed by a vehicle  10  in which the control system  120  controls at least one actuator system  130  of the vehicle  10  in accordance with sensor data from the sensor system  110 . Also, in  FIG.  2   , the control system  120  includes an obstacle detection system. In this example, the control system  120  is configured to detect an entity based on the sensor data and generate boundary data (e.g., outline, contour, or frame) that corresponds to the detection of the entity with respect to the sensor data. More specifically, upon receiving at least the sensor data and/or the boundary data of the entity that is detected from the sensor data, the processing system  160 , via the classifier  200  with its machine learning system  200 A, is configured to predict an identity of the entity and provide output data based on its prediction. As a non-limiting example, for instance, upon receiving sensor data and/or boundary data indicating at least one entity that is detected from among this sensor data, the processing system  160 , via the machine learning system  200 A, is configured to classify the detected entity as belonging to the pedestrian class from among several classes (e.g., traffic light, traffic sign, vehicle, animal, road structure, etc.) of obstacles. In addition, the processing system  160 , via the machine learning system  200 A, is configured to generate class data to indicate that the detected entity is a “pedestrian.” Also, in this non-limiting example, upon the generation of this class data via the classifier  200 , the control system  120  is configured to generate control data for the actuator system  170  to actuate at least one functional component of the vehicle  10  based on the class data that that identifies the detected entity that is being sensed in the environment of the vehicle  10 . For example, the actuator system  130  may include a steering system such that the control system  120  generates control data that relates to a steering action. As another example, the actuator system  130  may include a braking system such that the control system  120  generates control data that relates to a braking action. The actuator system  130  is not limited to a steering system and/or a braking system, but may include any actuator relating to the vehicle  10 . 
     In addition, the control system  120  is configured, via the detector  210 , to generate classification data for each sequence of inputs to the machine learning system  200 A. The classification data classifies the sequence of inputs as being (i) a nominal sequence comprising nominal elements or (ii) an adversarial sequence comprising adversarial elements. In this regard, the detector  210  is advantageous in identifying each nominal sequence that the machine learning system  200 A receives as input and enabling the system  100  to operate based on each corresponding sequence of output data that is generated by the machine learning system  200 A with a level of assurance that an adversarial attack was most likely absent during the generation of the corresponding sequence of output data. Also, the detector  210  is advantageous in identifying each adversarial sequence that the machine learning system  200 A receives as input and enabling the system  100  to take defensive action with respect to a possible adversarial attack and avoid using the each corresponding sequence of output data that was generated from the machine learning system  200 A during that time frame. 
     The control system  120  is also configured to provide this classification data from the detector  210  along with the corresponding output data from the machine learning system  200 A to the adversarial defense system  220 . In this regard, the adversarial defense system  220  is configured to handle the output data obtained from the machine learning system  200 A according to the classification data obtained from the detector  210 . For example, upon receiving a nominal label from the detector  210 , the adversarial defense system  220  is configured to identify output data from the machine learning system  200 A that is generated based on this sequence of inputs that correspond to the nominal label, and provide an indication that the control system  120  is to handle the output data in a nominal mode. In the nominal mode, the control system  120  is operable to generate control data based on the corresponding output data of the machine learning system  200 A. Alternatively, upon receiving an adversarial label from the detector  210 , the adversarial defense system  220  is configured to identify output data from the machine learning system  200 A that is generated based on the flagged sequence of inputs that correspond to the adversarial label, and provide an indication that the control system  120  is to handle the output data in defensive mode. In the defensive mode, the adversarial defense system  220  is configured to delay the output data, replace the output data with predetermined output data (e.g., default output data), reject the output data, filter out the output data, discard the output data, or take any suitable action that prevents the system  100  from acting on the output data that is generated based on the detected sequence of adversarial data. Upon receiving communications from the adversarial defense system  220  that selectively includes only sequences of output data from the machine learning system  200 A that corresponds to nominal sequences, the control system  120  is configured to generate control data that is based on these nominal sequences of output data. In response to the control data, the actuator system  130  is configured to control or assist with actuation of the vehicle  10 , which may be autonomous, highly-autonomous, partially-autonomous, conditionally-autonomous, or driver-assisted. 
     Additionally or alternatively to the example of  FIG.  2   , the system  100  (and/or control system  120 ) is also operable in other applications. For example, the system  100  and/or control system  120  is operable in various fields, such as computer-controlled machines, robots, home-appliances, power tools, electronic personal assistants, healthcare/medical technology, mobile machines, security technology, etc. That is, the system  100  and/or control system  120  is not limited to the above-mentioned applications, but can be applied to any suitable application that benefits from detecting an adversarial attack that employs iterative techniques that involve perturbations over a sequence of elements. 
       FIGS.  3 A and  3 B  illustrate conceptual diagrams of the interactions of some components of the system  100 , particularly the machine learning system  200 A, the detector  210 , and the adversarial defense system  220 . More specifically, in  FIG.  3 A , the control system  120  operates in the nominal mode when the detector  210  determines that a sequence of inputs is nominal and/or the adversarial defense system  220  indicates an absence of an adversarial attack. In contrast, in  FIG.  3 B , the control system  120  operates in the adversarial mode when the detector  210  determines that the sequence of inputs is adversarial and/or the adversarial defense system  220  indicates a presence of an adversarial attack. Furthermore, although not shown in  FIGS.  3 A and  3 B , the processing system  150  is actively engaged with these components during both the nominal mode and the defensive mode. 
       FIG.  3 A  illustrates an example of a scenario in which the control system  120  operates in a nominal mode. More specifically, the sensor system  110  provides a stream of sensor data based on its environment. The stream of sensor data includes a sequence of sensor data, which may be represented as X={x 1 , x 2 , . . . x t )}, where X represents the sequence and x 1  to x t  represent the elements of the sequence. For example, each element of the sequence X may refer to a frame of sensor data. Upon obtaining the sensor data, the processing system  150 , via the machine learning system  200 A, is configured to generate class data for the sensor data. The class data, which are output via the machine learning system  200 A for the stream of sensor data, may be represented as Y={y 1 , y 2 , . . . y t }, where Y represents the sequence and y 1  to y t  represent the elements of the sequence. For example, each element of the sequence Y may refer to class data that is generated by the machine learning system  200 A for each element of the sequence X. 
     The detector  210  is configured to receive the same input (e.g., X={x 1 , x 2 , . . . x t }) as the machine learning system  200 A. Upon receiving the sequence of sensor data as input, the detector  210  is configured to generate a nominal label upon predicting that the sequence of sensor data is a nominal sequence and generate an adversarial label upon predicting that the sequence of sensor data is an adversarial sequence. In this case, as shown in  FIG.  3 A , the detector  210  determines that the sequence of sensor data comprises nominal data and generates a nominal label for the input. The adversarial defense system  220  is configured to receive the nominal label from the detector  210  and the corresponding class data (Y={y 1 , y 2 , . . . y t }) from the machine learning system  200 A. In this case, since the detector  210  indicates that the machine learning system  200 A received a nominal sequence as input, the adversarial defense system  220  is operable to indicate that the control system  120  is configured to operate in a nominal mode such that control data for the actuator system  170  is generated for the actuator system based at least on the class data from the machine learning system  200 A. 
       FIG.  3 B  illustrates an example of a scenario in which the control system  120  operates in a defensive mode. Unlike  FIG.  3 A ,  FIG.  3 B  includes an adversary system  20 , which is not a part of the system  100  and which is generating adversarial attacks to the system  100 . In general, the adversary system  20  iteratively perturbs the sensor data to the machine learning system  200 A to cause the machine learning system  200 A to break down and/or fail. In this example, the adversary system  20  is operable to perturb the sensor data with perturbation data, which may be imperceptible, to an extent that the machine learning system  200 A generates class data for the perturbed version of the sensor data that is different than the class data that would have been generated by the machine learning system  200 A for an unperturbed version of that same sensor data. Often times, the adversary system  20  is unsuccessful in its first attempt to cause the machine learning system  200 A to fail, and thus makes several attempts at perturbing inputs to the machine learning system  200 A while using the output data (e.g., class data) of the machine learning system  200 A as feedback to determine the perturbation data on the input that will cause the machine learning system  200 A to fail. In this regard, the adversary system  20  typically relies on iterative techniques to achieve a successful adversarial attack. 
     As shown in  FIG.  3 B , the sensor system  110  generates a stream of sensor data based on its environment. The stream of sensor data includes a sequence of sensor data, which may be represented as X={x 1 , x 2 , . . . x t }, where X represents the nominal sequence and where x 1  to x t  represent the elements of the sequence. For example, each element may refer to a frame of sensor data. However, in this scenario, the adversary system  20  generates a sequence of perturbation data and perturbs the sensor data such that the machine learning system  200 A receives a perturbed version of the sensor data. For example, the sequence of perturbation data may be represented by δ={δ 1 , δ 2 , . . . δ t }, where S represents the sequence of perturbation data and where δ 1  to δ t  represent the various perturbation elements of that sequence. Also, the perturbed version of the sensor data may be represented as X′=(x′ 1 , x′ 2 , . . . x′ t ), where X′ represents the perturbed version of the sequence X and where x′ t  to x′ t  represent the perturbed elements of the sequence that have been perturbed by the sequence of perturbation data δ={δ 1 , δ 2 , . . . δ t }, respectively. Upon receiving the perturbed versions of the sensor data, the processing system  150 , via the machine learning system  200 A, is configured to generate class data that classifies these perturbed versions of the sensor data. In addition, the detector  210  is also configured to receive the same input (i.e., the perturbed sensor data of X′={x′ 1 , x′ 2 , . . . x′ t }) as the machine teaming system  200 A. Upon receiving the perturbed version of the sequence of sensor data as input, the detector  210  is configured to generate a nominal label upon predicting that the sequence of sensor data is a nominal sequence and generate an adversarial label upon predicting that the sequence of sensor data is an adversarial sequence. In this case, as shown in  FIG.  3 B , the detector  210  determines that the sequence of inputs (i.e., X′) to the machine learning system  200 A is an adversarial sequence and generates an adversarial label for that sequence of inputs (i.e., X′). The adversarial defense system  220  is configured to receive the adversarial label from the detector  210  and the corresponding sequence, Y′, of class data (Y′={y 1 , y 2 , . . . y t }) from the machine learning system  200 A based on timestamp data. In this case, since the detector  210  indicates that the sequence of inputs to the machine learning system  200 A is an adversarial sequence, the adversarial defense system  220  is configured to activate the defensive mode such that the corresponding sequence of class data, which is generated based on the flagged inputs, are filtered out and prevented from affecting downstream systems, such as the actuator system  170 . For example, in  FIG.  3 B , the adversarial defense system  220  does not permit use of the corresponding class data of Y′=(y 1 , y 2 , . . . y t ) as output data. 
       FIG.  4    is a diagram of a system  400  associated with training the detector  210  according to an example embodiment. In this simplified example, the system  400  includes at least a memory system  410  and a processing system  420 . In  FIG.  4   , the memory system  410  is a computer or electronic storage system, which is configured to store and provide access to various data to enable at least the operations and functionality, as disclosed herein. The memory system  410  comprises a single device or a plurality of devices. The memory system  410  includes electrical, electronic, magnetic, optical, semiconductor, electromagnetic, any suitable memory technology, or any combination thereof For instance, the memory system  410  includes RAM, ROM, flash memory, a disk drive, a memory card, an optical storage device, a magnetic storage device, a memory module, any suitable type of memory device, or any combination thereof. In an example embodiment, with respect to the processing system  420 , the memory system  410  is local, remote, or a combination thereof (e.g., partly local and partly remote). For example, the memory system  410  is configured to include at least a cloud-based storage system (e.g. cloud-based database system), which is remote from the processing system  420 . 
     In an example embodiment, as shown in  FIG.  4   , the memory system  410  includes the detector  210 , which includes the machine-learning system  210 A. Also, as shown in  FIG.  4   , the memory system  410  includes at least training data  412  and machine learning data  414 , which are used to generate the detector  210 . In addition, the memory system  410  is configured to include other relevant data, which relates to training and generating the detector  210  as discussed herein. More specifically, the training data  412  includes at least sensor data (and/or image data based on the sensor data). The machine learning data  414  includes machine learning algorithms associated a method  700  ( FIG.  7   ) for training and generating the detector  210 . The detector  210  includes the machine learning system  210 A along with various data (e.g., various layers, weights, parameter data, etc.), which are associated with the training and/or operation of its machine learning system  210 A. Once trained to perform at a predetermined level of accuracy, the detector  210  is deployable and/or employable by the system  100  of  FIG.  1    or any suitable application system. 
     Upon receiving training data  412 , the processing system  420  is configured to train the machine learning system  210 A according to the machine learning data  414 . In this regard, the processing system  420  includes at least one processor. For example, the processing system  420  includes an electronic processor, a CPU, a GPU, a microprocessor, a FPGA, an ASIC, processing circuits, any suitable processing technology, or any combination thereof. In an example embodiment, the processing system  420  communicates with the memory system  410  to generate the detector  210  based on the training data  412  and the machine learning data  414 . 
       FIG.  5    is a flow diagram associated with a training process  500  to generate the detector  210  according to an example embodiment. In general, the training process  500  involves a substantial and sufficient amount of training data  412  to ensure that the detector  210  performs accurately. For example, the collection of training data  412  includes at least a set of nominal sequences  412 A and a set of adversarial sequences  412 B. In addition, the training data  412  may include historical and/or actual adversarial attack data, which are collected from real-life adversarial attacks to various machine learning systems. 
     The set of nominal sequences  412 A includes at least sensor data, sensor-fusion data, image data based on sensor data, image data based on sensor-fusion data, or any combination thereof. Also, in this example, the set of adversarial sequences  412 B include at least one or more perturbed versions of the set of nominal sequences  412 A. In general, the set of adversarial sequences  412 B may include any sequence in which a plurality of elements thereof are perturbed by perturbations even if the sequence is not successful in causing a machine learning system to fail (e.g., misclassify the sequence such that f(x′)≠f(x), where f(x′) represents output data of the machine learning system based on the perturbed version of the element and f(x) represents output data of that machine learning system based on an unperturbed version of that same element). After completing this training process  500  with at least this collection of training data  412 , the detector  210 , via at least one processor, is configured to generate a nominal label upon predicting that a sequence is a nominal sequence (or unperturbed versions of sensor data) and generate an adversarial label upon predicting that the sequence is an adversarial sequence (or perturbed versions of that sensor data). 
       FIGS.  6 A and  6 B  illustrate examples of training data  412 . For example,  FIG.  6 A  illustrates a nominal sequence  600 , which includes elements as denoted by x 1  to x t . In this case, each element is a frame of sensor data. For instance, each element may be an image frame, as taken from a video stream. Also, as shown in  FIG.  6 A , the nominal sequence  600  is sequential with respect to time, which advances in the direction of the arrow.  FIG.  6 A  also illustrates an adversarial sequence  610 , which is generated from the nominal sequence  600 . In this regard, for example, upon receiving the nominal sequence  600 , the processing system  420  is configured to generate the adversarial sequence  610  by selecting an element (e.g., x i ) from the nominal sequence  600  and perturbing that element iteratively, whereby a plurality of perturbed versions (e.g., x′ i,1 =x i +δ 1 , . . . , x′ i,p =x i +δ p ) are generated to form the adversarial sequence  610 . In  FIG.  6 A , the selected element, x i , is perturbed ‘p’ times, where p represents the iteration at which the selected element generates the perturbed element x′ i,p (i.e., x i +δ p ), which causes the machine learning system  200 A to fail such that f(x i +δ p )≠f(x i ). In addition,  FIG.  6 A  also illustrates that an adversarial sequence  620 , which includes the adversarial sequence  610  as a subsequence, may be provided as training data  412 . 
       FIG.  6 B  also illustrates an adversarial sequence  630 , which is generated from the nominal sequence  600 . More specifically, the processing system  420  is configured to generate the adversarial sequence  630  by perturbing each element of the nominal sequence. In this case, the adversarial sequence  630  includes a perturbed version of a respective element of the nominal sequence  600  until the machine learning system  200 A fails. For example, the adversarial sequence  630  includes a perturbed version of the first element of the nominal sequence, a perturbed version of the second element of the nominal sequence, and so forth until the machine learning system  200 A fails. In addition,  FIG.  6 B  also illustrates an adversarial sequence  640 , which includes the adversarial sequence  630  as a subsequence. In this case, the processing system  420  is configured to generate at least these adversarial sequences  630  and  640  by perturbing the elements of the nominal sequence  600  with respective elements from an adversary signature of perturbations (e.g., δ 1 , δ 2  . . . , δ k ). 
     As discussed above,  FIGS.  6 A and  6 B  illustrate some examples of training data  412  that can be used to train the detector  210  during the training process  500 .  FIGS.  6 A and  6 B  are advantageous in that the processing system  420  is enabled to generate these adversarial sequences  610 ,  620 ,  630  and  640  upon obtaining at least one nominal sequence  600 . In addition, the processing system  420  is also configured to generate other adversarial sequences  610 ,  620 ,  630 , and  640  from the nominal sequence  600  by attacking the nominal sequence  600  with other adversary signatures involving other perturbations. However, the set of adversarial sequences  412 B is not limited to the above-mentioned adversarial sequences  610 ,  620 ,  630 , and  640  (and/or adversarial sequences that cause the machine learning system  200 A to fail), but can include any adversarial sequence that includes a sequence of perturbed elements. In general, the detector  210  benefits by being trained with as much training data  412  as possible to an extent that its ability to discriminate between a nominal sequence and an adversarial sequence is enhanced. 
       FIG.  7    illustrates a flow diagram of an example of the training process  500  ( FIG.  5   ) for generating the detector  210  according to an example embodiment. This training process  500  includes a method  700  for training at least one machine learning system  210 A of the detector  210  to differentiate between at least one sequence of nominal data and at least one sequence of adversarial data. Advantageously, this method  700  provides training data  412  that includes both a set of nominal sequences  412 A and a set of adversarial sequences  412 B while also optimizing parameters of the machine learning system  210 A of the detector  210  based on results obtained from this training data  412 . Accordingly, upon undergoing the training process  500  with this method  700 , the detector  210  becomes operable to identify a sequence, predict whether or not the sequence is nominal/adversarial, and provide a label indicative of its prediction. 
     At step  702 , the processing system  420 , via the detector  210 , obtains a first set of training data. For example, the first set of training data includes a sufficient amount of nominal data to train the detector  210  such that the machine learning system  210 A is configured to operate with a predetermined level of accuracy. More specifically, the first set of training data includes a set of nominal sequences  412 A in which each sequence includes nominal data that is unperturbed by perturbation data. As discussed above, for instance, the nominal data includes sensor data, sensor-fusion data, image data based on sensor data, image data based on sensor-fusion data, or any combination thereof. Upon obtaining a set of nominal sequences  412 A as training data  412 , the method  700  proceeds to step  706 . 
     At step  704 , the processing system  420 , via the detector  210 , obtains a second set of training data. For example, the second set of training data includes a sufficient amount of adversarial data to train the detector  210  such that the machine learning system  210 A is configured to operate with a predetermined level of accuracy. More specifically, the second set of training data includes a set of adversarial sequences  412 B in which each sequence includes nominal data that is perturbed by perturbation data. In this regard, for instance, each adversarial sequence includes a plurality of perturbed sensor data, perturbed sensor-fusion data, perturbed image data based on sensor data, perturbed image data based on sensor-fusion data, or any combination thereof. In general, the adversarial sequences correspond to the nominal sequences, but further include perturbations on the elements. Upon obtaining a set of adversarial sequences  412 B as training data  412 , the method  700  proceeds to step  708 . 
     At step  706 , the processing system  420 , via the detector  210 , classifies each sequence from the set of nominal sequences, which may be referred to as the first set of training data. The processing system  420 , via the detector, is operable to analyze a sequence and assign one of the classes to that sequence. For example, the processing system  420 , via the detector  210 , is configured to evaluate a sequence from the set of nominal sequences and determine by its machine learning model that the sequence belongs to the nominal class or the adversarial class. 
     At step  708 , the processing system  420 , via the detector  210 , classifies each sequence from the set of adversarial sequences, which may be referred to as the second set of training data. The processing system  420 , via the detector, is operable to analyze a sequence and assign one of the classes to that sequence. For example, the processing system  420 , via the detector  210 , is configured to evaluate a sequence from the set of adversarial sequences and determine by its machine learning model that the sequence belongs to the nominal class or the adversarial class. 
     At step  710 , the processing system  420 , via the detector  210 , generates classification data based on the first set of training data. In this case, the first set of training data includes the set of nominal sequences  412 A. The detector  210  is operable to generate a nominal label for an input upon predicting that the input is a sequence of nominal data (or a sequence of non-adversarial data) and generate an adversarial label for that input upon predicting that the input is a sequence of adversarial data (or a sequence of non-nominal data). In this regard, for instance, the nominal label may be represented by one binary symbol (e.g., zero) and the adversarial label may be represented by another binary symbol (e.g., one), or vice versa. In this case, since each input to the detector  210  is a nominal sequence from the first set of training data, the processing system  420  is enabled to compare the true classification data of a nominal label for a sequence of the first set with the predicted classification data (e.g. nominal label or adversarial label) for that sequence of the first set. 
     At step  712 , the processing system  420 , via the detector  210 , generates classification data based on the second set of training data. In this case, the second set of training data includes the set of adversarial sequences  412 B. As aforementioned, the detector  210  is operable to generate a nominal label for an input upon predicting that the input is a sequence of nominal data (or a sequence of non-adversarial data) and generate an adversarial label for that input upon predicting that the input is a sequence of adversarial data (or a sequence of non-nominal data). In this regard, consistent with step  710 , the nominal label may be represented by one binary symbol (e.g., zero) and the adversarial label may be represented by another binary symbol (e.g., one). In this case, since each input to the detector  210  is an adversarial sequence from the second set of training data, the processing system  420  is enabled to compare the true classification data of an adversarial label for a sequence of the second set with the predicted classification data (e.g. nominal label or adversarial label) for that sequence of the second set. 
     At step  714 , the processing system  420  generates average loss data of the detector  210  that relates to a difference between the predicted classifications and the true classifications of the first set of training data. More specifically, the processing system  420  evaluates incorrect classification data relative to correct classification data that is generated for the first set of training data (e.g., set of nominal sequences  412 A). More specifically, with respect to this first set of training data, the detector  210  generates (i) correct classification data when a nominal label is predicted via machine learning system  210 A upon receiving one of these nominal sequences as input and (ii) incorrect classification data when an adversarial label is predicted via machine learning system  210 A upon receiving one of these nominal sequences as input. For mere convenience, this average loss data may be referred to as the “first average loss data.” 
     At step  716 , the processing system  420  generates average loss data of the detector  210  that relates to a difference between the predicted classifications and the true classifications of the second set of training data. More specifically, the processing system  420  evaluates incorrect classification data relative to correct classification data that is generated for the second set of training data (e.g., set of adversarial sequences  412 B). More specifically, with respect to this second set of training data, the detector  210  generates (i) correct classification data when an adversarial label is predicted via machine learning system  210 A upon receiving one of the adversarial sequences as input and (ii) incorrect classification data when a nominal label is predicted via machine learning system  210 A upon receiving one of the adversarial sequences as input. For mere convenience, this average loss data may be referred to as the “second average loss data.” 
     At step  718 , the processing system  420  optimizes parameters of a discriminator of the detector  210  based on a relative weighted function involving the first average loss data and the second average loss data. More specifically, for example, the processing system  420  optimizes the parameters (e.g., θ), associated with a discriminator (e.g., discriminative model or network) of the machine learning system  210 A, which is parametrized by θ and which is represented by d 0  in the following equation: 
     
       
         
           
             
               
                 
                   
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     In this equation, the processing system  420  determines the values of the parameters (e.g., θ) of the discriminator of the detector  210  for which the combined loss, 
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attains its minimum via the argmin function. In this case, the combined loss includes the first average loss data
 
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Also, in this equation, the loss function is represented by L(y p , y c ), which minimizes the error between output data y p , and output data y c , where output data y p  represents the predicted classification data (e.g., nominal/adversarial label) of the input sequence and where output data y c  represents the true classification data (e.g., nominal/adversarial label) of the input sequence.
 
     Also, as indicated by equation (1), the processing system  420 , via the detector  210 , includes at least one machine learning model that performs binary sequence classification to identify whether or not an adversarial signature is present with respect to a sequence of input data. For instance, using X test  as a test sequence of input for convenience of explanation, the detector  210  is configured to assign one binary value (e.g., the value of zero) to the test sequence as a nominal label upon determining that d(X test )=0 based on a prediction that the test sequence is a nominal sequence. In addition, the detector  210  is configured to assign the other binary value (e.g., the value of one) to the test sequence as an adversarial label upon determining that d(X test )=1 based on a prediction that the test sequence is an adversarial sequence. Alternatively, the detector  210  may be configured to assign any set of values (e.g., zero and one) as classification data (e.g., nominal label and adversarial label) provided that the detector  210  operates as described herein. 
     In equation (1), λ represents a parameter that provides a relative weighting between the first average loss and the second average loss. The parameter, λ, enables the processing system  420  to adjust a balance of the relative weight of the misclassifications of the nominal data (e.g. nominal sequences) and the misclassifications of adversarial data (e.g., adversarial sequences) in accordance with the application. In this regard, the parameter, λ, provides a balancing factor between detections of nominal sequences (e.g., nominal sensor data) and detection of adversarial sequences (and/or adversarial attacks). 
     At step  720 , the processing system  420  provides the detector  210 , which is ready for deployment/employment, upon completing its training with optimized parameters. In this regard, after the parameters have been optimized and/or the detector  210  has been trained with the optimized parameters, the detector  210  is configured to be deployed/employed by the system  100  or any suitable system upon evaluating that the detector  210  operates with a predetermined level of accuracy. Also, once trained, the detector  210  with its machine learning system  210 A, is advantageously enabled, via at least one processor, to predict by statistical determination and/or probabilistic means whether a sequence warrants a nominal label or an adversarial label, thereby providing an indication of the absence or presence of an adversarial attack. 
     Furthermore, various modifications can be made to the above-mentioned embodiments without departing from the spirit and scope of these embodiments. For example, in  FIG.  1   , instead of the classifier  200  and the machine learning system  200 A associated therewith, the system  100  may include any software module with a trained machine learning system that is suitable for the intended application. That is, the detector  210  and adversarial defense system  220  are configured to provide the same or substantially similar advantages to any machine learning system and/or software system that relies on sequences of inputs that may be susceptible to adversarial attacks. 
     In addition, as another example of a modification, additionally or alternatively to synthetic types of attacks, the set of adversarial sequences  412 B may include real-life adversarial attack data, which are acquired from actual adversarial attacks to various machine learning systems. Also, as yet another example of a modification, the set of adversarial sequences  412 B is not limited to the examples of  FIGS.  6 A and  6 B , but may include any perturbed version of a nominal sequence in which a plurality of perturbations occur over a plurality of elements (or frames) of that sequence. Additionally, as yet another example of a modification, the process of generating adversarial sequences is combinable with the training process  500  such that new adversarial sequences can be generated as a training set for each iteration of training the detector  210 . Furthermore, the training process  500  can be performed in a plurality of iterations and a plurality of batches. Also, as yet another example of a modification, during the training process  500 , the detector  210  and the adversary system  20  may be configured to create a zero-sum game, where the detector  210  is operable to minimize the combined loss while the adversary system  20  is operable to maximize the combined loss (e.g., making a sequence of adversarial data undetectable), thereby training the detector  210  to become more robust by addressing a more robust adversary system  20 . 
     As described herein, the embodiments include a number of advantageous features and benefits. For example, the embodiments are advantageous in determining whether a sequence of inputs to the machine learning system  200 A is a sequence of queries, which has an adversarial goal in the sense of understanding model limitations and/or learning perturbations to the input data that cause the machine learning system  200 A to fail. In this regard, the embodiments are advantageous in addressing the technical problem of determining whether a sequence that is input to at least one machine learning system  200 A is associated with nominal data (e.g., a sensor stream from a sensor) or adversarial data (e.g., a perturbed version of the sensor stream with adversarial queries from an adversary system  20 ) as a means of detecting an absence/presence of an adversarial attack. Upon detecting an adversarial sequence, the detector  210  is operable to flag its adversarial detections so that the system  100  is enabled to respond to the adversarial attack. As one example, for instance, the adversarial defense system  220  is activated to filter out a corresponding sequence of output data that is generated by the machine learning system  200 A based on the adversarial sequence so that effects resulting from the adversarial sequence are avoided and/or not realized by another system that would otherwise receive this output data from the machine learning system  200 A. For instance, the system  100  is operable to prevent incorrect output data (e.g., incorrect class data), which is based on a detected sequence of adversarial data, from affecting the actuator system  170 , thereby providing an added level of security and safety to the system  100  with respect to adversarial attacks. 
     That is, the above description is intended to be illustrative, and not restrictive, and provided in the context of a particular application and its requirements. Those skilled in the art can appreciate from the foregoing description that the present invention may be implemented in a variety of forms, and that the various embodiments may be implemented alone or in combination. Therefore, while the embodiments of the present invention have been described in connection with particular examples thereof, the general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the described embodiments, and the true scope of the embodiments and/or methods of the present invention are not limited to the embodiments shown and described, since various modifications will become apparent to the skilled practitioner upon a study of the drawings, specification, and following claims. For example, components and functionality may be separated or combined differently than in the manner of the various described embodiments, and may be described using different terminology. These and other variations, modifications, additions, and improvements may fall within the scope of the disclosure as defined in the claims that follow.