Detecting Patch Adversarial Attacks with Image Residuals
We introduce an adversarial sample detection algorithm based on image residuals, specifically designed to guard against patch-based attacks. The image residual is obtained as the difference between an input image and a denoised version of it, and a discriminator is trained to distinguish between clean and adversarial samples. More precisely, we use a wavelet domain algorithm for denoising images and demonstrate that the obtained residuals act as a digital fingerprint for adversarial attacks. To emulate the limitations of a physical adversary, we evaluate the performance of our approach against localized (patch-based) adversarial attacks, including in settings where the adversary has complete knowledge about the detection scheme. Our results show that the proposed detection method generalizes to previously unseen, stronger attacks and that it is able to reduce the success rate (conversely, increase the computational effort) of an adaptive attacker.
Code (1)
Tasks
DenoisingSimilar Papers 제목 키워드 기반
PatchZero: Defending against Adversarial Patch Attacks by Detecting and Zeroing the Patch
Adversarial patch attacks mislead neural networks by injecting adversarial pixels within a local region. Patch attacks can be highly effective in a variety of tasks and physically realizable via attachment (e.g. a sticke…
Diversityimage-classificationImage Classificationobject-detection+3We Can Always Catch You: Detecting Adversarial Patched Objects WITH or WITHOUT Signature
Recently, the object detection based on deep learning has proven to be vulnerable to adversarial patch attacks. The attackers holding a specially crafted patch can hide themselves from the state-of-the-art person detecto…
object-detectionObject DetectionAdversarial YOLO: Defense Human Detection Patch Attacks via Detecting Adversarial Patches
The security of object detection systems has attracted increasing attention, especially when facing adversarial patch attacks. Since patch attacks change the pixels in a restricted area on objects, they are easy to imple…
Human Detectionimage-classificationImage Classificationobject-detection+1X-Detect: Explainable Adversarial Patch Detection for Object Detectors in Retail
Object detection models, which are widely used in various domains (such as retail), have been shown to be vulnerable to adversarial attacks. Existing methods for detecting adversarial attacks on object detectors have had…
Decision MakingObjectobject-detectionObject DetectionGenerative Dynamic Patch Attack
Adversarial patch attack is a family of attack algorithms that perturb a part of image to fool a deep neural network model. Existing patch attacks mostly consider injecting adversarial patches at input-agnostic locations…