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Reinforcement Learning Inspired Black-box Adversarial Attacks for Computer Vision

arXiv · AI, language, vision and robotics · article · Sep 21, 2026 · UTC

Neural networks, both convolution or transformer based, are essential for modern computer vision systems. However, they are vulnerable to small perturbations, almost imperceptible to humans, which significantly alter the model's prediction. These adversarial attacks are often considered to be a significant threat to the implementation of neural networks in safety-critical applications. Most attacks utilize the white-box threat model and therefore require full access to the target model, making them unrealistic to use in practice. We propose a novel approach under the more realistic black-box t

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