Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors (1910.14667v2)

Abstract: We present a systematic study of adversarial attacks on state-of-the-art object detection frameworks. Using standard detection datasets, we train patterns that suppress the objectness scores produced by a range of commonly used detectors, and ensembles of detectors. Through extensive experiments, we benchmark the effectiveness of adversarially trained patches under both white-box and black-box settings, and quantify transferability of attacks between datasets, object classes, and detector models. Finally, we present a detailed study of physical world attacks using printed posters and wearable clothes, and rigorously quantify the performance of such attacks with different metrics.

• The paper demonstrates a novel adversarial method using learned patches that reduces YOLOv2-r’s average precision to 7.5% in digital tests.
• The paper evaluates both white-box and black-box attacks across multiple detection frameworks, including YOLO and Faster RCNN, to assess transferability.
• The paper shows that while physical adversarial patches are less effective due to environmental factors, they still pose real-world security risks.

Adversarial Attacks on Object Detectors: An Examination of Invisibility Cloak Strategies

In the paper "Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors," Zuxuan Wu et al. provide an in-depth exploration of the transferability and feasibility of adversarial attacks against state-of-the-art object detection systems. The authors aim to bridge the gap between digital adversarial attacks and their real-world applicability, employing methods to disrupt object detection frameworks by minimizing objectness scores through adversarial patches.

Adversarial attacks typically involve crafting inputs that induce erroneous outputs in machine learning models. While these have been significantly studied in the context of classifiers, their application to object detectors presents additional challenges due to the structural complexity of such models. Object detectors calculate objectness scores across multiple overlapping candidates, which requires crafted attacks to affect numerous points within the feature map simultaneously to be effective.

In their paper, the authors focus on the transferability of adversarial attacks across various settings, including different models, datasets, and environments. They employ both white-box (where the attacker has full model knowledge) and black-box (where the model's internal details are obscured) attack paradigms while experimenting with diverse detection frameworks such as YOLOv2, YOLOv3, and variants of Faster RCNN. By leveraging ensemble learning during the patch training process, the researchers improve attack generalizability, demonstrating that effective attacks need not be tailored to a single model's architecture.

Key numerical findings reveal that these learned adversarial patches significantly degrade the performance of object detectors in digital simulations. For example, when tested on the retrained model YOLOv2-r, the average precision (AP) fell to just 7.5%, showcasing the efficacy of these approaches in digital environments. Moreover, the research indicates that adversarial patches degrade detector performance across various backbone architectures. This finding underscores the vulnerability of object detectors to adversarial manipulations, despite their intrinsic robustness to minor input fluctuations.

The paper ventures into the practical field by conducting extensive experiments with printed adversarial patterns—posters and T-shirts—to evaluate the feasibility of adversarial attacks in physical settings. Notably, while digital attacks demonstrated considerable success, the transfer of these attacks to the physical world showcased variability and reduced effectiveness. This discrepancy points to real-world nuisances such as lighting, distance, and fabric deformations as factors that complicate attack efficacy. Despite this, the paper demonstrates that wearable adversarial examples can still achieve significant success rates against detectors, emphasizing the need for continued investigation into physical adversarial machine learning.

The implications of this research are profound, suggesting a dual pathway for future developments in AI security: enhancing model robustness against adversarial inputs and concurrently designing more sophisticated attack mechanisms to test and strengthen such defenses. Furthermore, as adversarial attacks become increasingly capable of real-world applications, there is an exigency to anticipate and mitigate potential security risks associated with the deployment of machine learning systems in safety-critical applications.

In conclusion, the systematic paper conducted in this paper provides a comprehensive evaluation of adversarial attacks on object detection systems, bridging theoretical digital scenarios with practical, physical realities. The insights gleaned from this work present valuable knowledge for the AI research community, paving the way for advanced defensive strategies against potential adversarial threats.