DPatch: An Adversarial Patch Attack on Object Detectors (1806.02299v4)

Abstract: Object detectors have emerged as an indispensable module in modern computer vision systems. In this work, we propose DPatch -- a black-box adversarial-patch-based attack towards mainstream object detectors (i.e. Faster R-CNN and YOLO). Unlike the original adversarial patch that only manipulates image-level classifier, our DPatch simultaneously attacks the bounding box regression and object classification so as to disable their predictions. Compared to prior works, DPatch has several appealing properties: (1) DPatch can perform both untargeted and targeted effective attacks, degrading the mAP of Faster R-CNN and YOLO from 75.10% and 65.7% down to below 1%, respectively. (2) DPatch is small in size and its attacking effect is location-independent, making it very practical to implement real-world attacks. (3) DPatch demonstrates great transferability among different detectors as well as training datasets. For example, DPatch that is trained on Faster R-CNN can effectively attack YOLO, and vice versa. Extensive evaluations imply that DPatch can perform effective attacks under black-box setup, i.e., even without the knowledge of the attacked network's architectures and parameters. Successful realization of DPatch also illustrates the intrinsic vulnerability of the modern detector architectures to such patch-based adversarial attacks.

• The paper demonstrates that DPatch reduces mAP from over 65% to below 1%, effectively crippling both object classification and bounding box regression.
• It introduces a small, position-independent patch attack that works across models like Faster R-CNN and YOLO, highlighting practical real-world threats.
• The study reveals the transferability of the attack across different architectures, underscoring the need for robust defense strategies in detection systems.

Analyzing the Efficacy of DPatch: An Adversarial Patch Attack on Object Detectors

The paper "DPatch: An Adversarial Patch Attack on Object Detectors" introduces a novel method for compromising the integrity of contemporary object detectors such as Faster R-CNN and YOLO. Unlike traditional approaches which employ full-image perturbations, this paper advances a localized, adversarial patch-based attack capable of simultaneously disrupting both object classification and bounding box regression, thereby significantly undermining detection performance.

Key Contributions and Findings

1. Attack Performance: The paper demonstrates that DPatch can execute both untargeted and targeted attacks effectively, reducing the mean Average Precision (mAP) of Faster R-CNN and YOLO from 75.10% and 65.7% to below 1%, respectively. This indicates a severe degradation in detection capability when DPatch is employed.
2. Practical Implementation: DPatch is both small in size and independent of position within the input image, thus enhancing its practicality for real-world applications. The patch’s design allows it to disrupt detection without being contingent on its placement in the scene.
3. Transferability: A notable finding is DPatch's transferability across different models and datasets. For instance, a DPatch trained on Faster R-CNN successfully disrupted YOLO and vice versa, implying that the method generalizes well across various detector architectures.
4. Inherent Vulnerabilities: The paper underscores the vulnerabilities intrinsic to modern detector architectures when subjected to patch-based adversarial attacks, even under black-box conditions where attackers access neither the architecture nor the parameters of the target networks.

Implications for Computer Vision

The proposed adversarial patch technique introduces significant considerations for the robustness and security of artificial intelligence systems reliant on object detection. It accentuates the urgency for developing fortified networks capable of resisting such adversarial manipulations, which pose threats particularly in applications involving autonomous vehicles and surveillance systems.

Further, the demonstrated transferability raises concerns about the adaptability of detectors when exposed to unseen adversarial inputs, necessitating enhanced training and model hardening strategies. DPatch's ability to maintain high efficacy even with minimal model knowledge (black-box setting) presents an exigent case for incorporating robust defenses in AI systems against unforeseen adversarial methods.

Prospective Research Directions

Future investigations could delve into refining defense mechanisms to not only identify such adversarial patches but also sustain detection efficacy in their presence. The development of detection algorithms with intrinsic resilience to adversarial manipulations, possibly through adversarial training or model architecture innovation, warrants thorough exploration.

Moreover, further exploration into the scalability of such patch-like attacks across diverse model architectures and datasets will be critical. As adversarial strategies evolve, understanding their broad implications on the designs and security of neural architectures will be essential.

In summation, the research presented in this paper profoundly enlightens the vulnerabilities in current object detection systems, thereby contributing to the ongoing dialogue around security in AI and re-affirming the necessity for continued advancements in adversarial defense strategies.