Sliding Window Gradient Sign Attack

An adversarial attack is an intentional attempt to fool or affect a machine learning model by giving deliberately generated input data, often known as adversarial examples. An adversarial attack exploits flaws in the model's decision-making process, resulting in inaccurate or unexpected results. Attacks on deep learning models, especially in computer vision, threaten their reliability. This work introduces a novel adversarial attack technique. This attack effectively targets vulnerable areas by producing imperceptible perturbations localised to particular sections of an input image by utilising the gradient sign idea. The approach drastically lowers the accuracy of deep learning algorithms by applying gradient-based perturbations throughout the image using the sliding window technique. Extensive experiments on state-of-the-art deep learning architecture were conducted to assess the feasibility of the suggested attack. We identified the specific weak points in the input photos by varying several parameters to control the degree of disturbances. The findings indicate that the nose and eyes are among those parts of the face most susceptible to hostile attacks.