Vitranspad: Video Transformer Using Convolution and Self-Attention For Face Presentation Attack Detection

Most works on adversarial attacks consider that small images whose size already fits the model but downscaling is a necessary first step to adapt the size of the image to the model, and it can reform the adversarial signal. This paper explores attacking large images on classifiers with different input sizes and compares theoretical results with practical ones. The possibility of forging adversarial images using different interpolation methods and different deep learning structures are investigated. The distortion of the adversarial signal and the transferability over other downscaling methods are also studied. An ensemble model gathering different resizing interpolations is also proposed to increase the transferability of the attack against a set of downscaling kernels.