Defending 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 improved dramatically, they are vulnerable to adversarial patch attacks. Changing the pixels in a restricted region can easily fool the person detection network in safety-critical applications such as autonomous driving and security systems. Despite the necessity of countering adversarial patch attacks, very few efforts have been dedicated to defending person detection against adversarial patch attack. In this paper, we propose a novel defense strategy that defends against an adversarial patch attack by optimizing a defensive frame for person detection. The defensive frame alleviates the effect of the adversarial patch while maintaining person detection performance with clean person. The proposed defensive frame in the person detection is generated with a competitive learning algorithm which makes an iterative competition between detection threatening module and detection shielding module in person detection. Comprehensive experimental results demonstrate that the proposed method effectively defends person detection against adversarial patch attacks.
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous DrivingHuman Detectionobject-detectionObject DetectionSimilar Papers 제목 키워드 기반
Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection
Object detection plays a key role in many security-critical systems. Adversarial patch attacks, which are easy to implement in the physical world, pose a serious threat to state-of-the-art object detectors. Developing re…
Adversarial Attack DetectionAdversarial DefenseAdversarial RobustnessObject+3Defending against Patch-Based and Texture-Based Adversarial Attacks with Spectral Decomposition
Adversarial examples present significant challenges to the security of Deep Neural Network (DNN) applications. Specifically, there are patch-based and texture-based attacks that are usually used to craft physical-world a…
Defending Against Physical Adversarial Patch Attacks on Infrared Human Detection
Infrared detection is an emerging technique for safety-critical tasks owing to its remarkable anti-interference capability. However, recent studies have revealed that it is vulnerable to physically-realizable adversarial…
Data AugmentationHuman DetectionPatchZero: 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+3Defending Adversarial Patches via Joint Region Localizing and Inpainting
Deep neural networks are successfully used in various applications, but show their vulnerability to adversarial examples. With the development of adversarial patches, the feasibility of attacks in physical scenes increas…