Robustness Analysis against Adversarial Patch Attacks in Fully Unmanned Stores (2505.08835v1)

Abstract: The advent of convenient and efficient fully unmanned stores equipped with artificial intelligence-based automated checkout systems marks a new era in retail. However, these systems have inherent artificial intelligence security vulnerabilities, which are exploited via adversarial patch attacks, particularly in physical environments. This study demonstrated that adversarial patches can severely disrupt object detection models used in unmanned stores, leading to issues such as theft, inventory discrepancies, and interference. We investigated three types of adversarial patch attacks -- Hiding, Creating, and Altering attacks -- and highlighted their effectiveness. We also introduce the novel color histogram similarity loss function by leveraging attacker knowledge of the color information of a target class object. Besides the traditional confusion-matrix-based attack success rate, we introduce a new bounding-boxes-based metric to analyze the practical impact of these attacks. Starting with attacks on object detection models trained on snack and fruit datasets in a digital environment, we evaluated the effectiveness of adversarial patches in a physical testbed that mimicked a real unmanned store with RGB cameras and realistic conditions. Furthermore, we assessed the robustness of these attacks in black-box scenarios, demonstrating that shadow attacks can enhance success rates of attacks even without direct access to model parameters. Our study underscores the necessity for robust defense strategies to protect unmanned stores from adversarial threats. Highlighting the limitations of the current defense mechanisms in real-time detection systems and discussing various proactive measures, we provide insights into improving the robustness of object detection models and fortifying unmanned retail environments against these attacks.

Robustness Analysis against Adversarial Patch Attacks in Fully Unmanned Stores

The utilization of artificial intelligence-based object detection technologies in fully unmanned stores presents a significant advancement in retail automation. However, these systems are increasingly susceptible to adversarial patch attacks, which exploit inherent vulnerabilities of deep neural networks (DNNs) in AI security. The paper "Robustness Analysis against Adversarial Patch Attacks in Fully Unmanned Stores" presents an in-depth investigation into how adversarial patches can critically disrupt object detection models within unmanned retail environments, resulting in practical security threats such as theft, inventory discrepancies, and interference.

The primary focus of this paper is the efficacy of adversarial patch attacks across three distinct attack types: Hiding, Creating, and Altering attacks. The paper introduces an innovative color histogram similarity loss function as a means to enhance attack performance, leveraging knowledge of the target object's color information. This novel approach represents a significant improvement over traditional adversarial detection strategies, enhancing the ability of patches to affect detection outcomes. The research uses metrics beyond the conventional confusion-matrix-based attack success rate, proposing a bounding-box-based analysis to better gauge the real-world impact of these attacks.

The experimental setup involves object detection models trained on datasets consisting of snacks and fruits, both in digital environments and a physical testbed designed to emulate real unmanned stores equipped with RGB cameras. The results demonstrate varied efficiency of these attacks based on the characteristics of the target class and the deployment environment. Notably, adversarial patches engineered with the proposed loss function led to significant alterations in detection, predominantly influencing classes with identifiable vulnerabilities.

Furthermore, the paper emphasizes that black-box scenarios pose complexity due to limited access to model parameters. However, shadow attacks, which utilize approximate model training based on the iterative querying of object detection outputs, have been shown to considerably enhance attack success rates, even without direct access to the target model. This indicates a substantial avenue for security risks in practical deployment scenarios where models are exposed, making robust defenses paramount.

The findings underscored in this paper highlight several implications for both the theoretical understanding and practical application of AI security strategies within unmanned stores. From a technical perspective, the introduction of color histogram similarity in adversarial training suggests new methodologies for crafting more effective adversarial patches. Practically, the insights gained from these robust analyses offer critical information for the development of countermeasures against adversarial threats, urging the integration of enhanced defenses in real-time detection systems.

Future work in AI robustness should consider expanding upon the loss functions used in adversarial patch generation to optimize the complexity and stealthiness of patches. Additionally, further exploration into class-specific vulnerabilities could improve the resilience of detection systems against adversarial manipulations. Overall, this paper contributes to the growing body of research addressing AI security, offering tangible pathways to securing the evolving landscape of automated retail environments from adversarial threats.