Practical Adversarial Attacks on Industrial AI: Evaluating Camera Sticker Techniques with MVTec AD

Cyber attacks on AI systems are recognized as a significant threat. In particular, adversarial attacks, which digitally perturb input data to cause AI to make incorrect predictions, are a serious concern. Recently, such attacks have been pointed out to be applied in the physical world. For instance, attackers place transparent stickers with adversarial perturbations on a camera lens to disrupt AI image perception. When AI-based factory automation (FA) systems are exposed to such threats, the AI systems can, for example, fail to detect defective products or internal system faults. These failures can lead to economic losses and system disruptions. In this paper, we implement the physical adversarial attacks and confirm their feasibility against AI-based FA systems through two experiments: (1) simulation experiments using the open-source FA-specific dataset (MVTec AD), and (2) a real-world experiment where physical stickers perturb camera lenses. As a result, in both evaluations, it is quantitatively shown that our attacks successfully caused malfunction in AI-based FA systems and that images perturbed by our method maintained high similarity to the original images. These experimental results reveal that adversarial attacks are feasible and pose a serious threat to FA systems.