Visual security defense for industrial inspection based on computer vision
Zhihao Jiang, Haotian Yuan, Chenrui Zeng, Liu Fu
TL;DR
This paper introduces a new security threat to industrial computer vision systems using hidden image layers and proposes a defense to detect it.
Contribution
The paper introduces the Alpha Channel Attack and a lightweight defense mechanism with high detection accuracy.
Findings
Alpha-channel perturbations significantly degrade detection and generation metrics without altering visible content.
The proposed defense achieves an AUC of 0.998 in identifying adversarial samples.
The attack affects various model architectures, including vision-language models.
Abstract
As intelligent manufacturing advances, computer vision-based defect detection systems have become essential components of industrial automation. However, this progress has also revealed new security vulnerabilities. In this work, we identify and examine a stealthy adversarial vector—the Alpha Channel Attack—which exploits the often-ignored transparency layer in RGBA images to inject imperceptible perturbations, thereby evading both human perception and conventional preprocessing defenses.We evaluate this threat across diverse model architectures, including YOLOv5, FastGAN, and state-of-the-art vision-language models such as DeepSeek-VL2, ChatGPT-4o, and KIMI. Experimental results show that alpha-channel perturbations cause substantial degradation in detection, generation, and multimodal alignment metrics—including mAP, FID, BLEU, METEOR, and CLIP Score—while leaving the visible image…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Advanced Neural Network Applications · Digital Media Forensic Detection