SlowFormer: Universal Adversarial Patch for Attack on Compute and Energy Efficiency of Inference Efficient Vision Transformers
Recently, there has been a lot of progress in reducing the computation of deep models at inference time. These methods can reduce both the computational needs and power usage of deep models. Some of these approaches adaptively scale the compute based on the input instance. We show that such models can be vulnerable to a universal adversarial patch attack, where the attacker optimizes for a patch that when pasted on any image, can increase the compute and power consumption of the model. We run experiments with three different efficient vision transformer methods showing that in some cases, the attacker can increase the computation to the maximum possible level by simply pasting a patch that occupies only 8\% of the image area. We also show that a standard adversarial training defense method can reduce some of the attack's success. We believe adaptive efficient methods will be necessary for the future to lower the power usage of deep models, so we hope our paper encourages the community to study the robustness of these methods and develop better defense methods for the proposed attack.
Code (1)
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
SlowFormer: Adversarial Attack on Compute and Energy Consumption of Efficient Vision Transformers
Recently there has been a lot of progress in reducing the computation of deep models at inference time. These methods can reduce both the computational needs and power usage of deep models. Some of these approaches a…
Adversarial AttackMeta Adversarial Training against Universal Patches
Recently demonstrated physical-world adversarial attacks have exposed vulnerabilities in perception systems that pose severe risks for safety-critical applications such as autonomous driving. These attacks place adversar…
Autonomous Drivingimage-classificationImage ClassificationMeta-LearningDefending Person Detection Against Adversarial Patch Attack by using Universal Defensive Frame
Person detection has attracted great attention in the computer vision area and is an imperative element in human-centric computer vision. Although the predictive performances of person detection networks have been improv…
Autonomous DrivingHuman Detectionobject-detectionObject DetectionPhysical Passive Patch Adversarial Attacks on Visual Odometry Systems
Deep neural networks are known to be susceptible to adversarial perturbations -- small perturbations that alter the output of the network and exist under strict norm limitations. While such perturbations are usually disc…
Autonomous NavigationDrone navigationVisual OdometryTnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems
Deep neural networks are vulnerable to attacks from adversarial inputs and, more recently, Trojans to misguide or hijack the model's decision. We expose the existence of an intriguing class of spatially bounded, physical…