A novel attack framework, LogoStyleFool, is proposed to fool video recognition systems by superimposing a stylized logo on the input video, achieving superior attack performance and semantic preservation compared to existing patch-based attacks.
StyleFool, a black-box video adversarial attack framework, leverages style transfer to efficiently fool video classification systems with unrestricted perturbations.