Dual Source Blending Attack

Dual-source blending attacks involve combining data from multiple sources to create malicious inputs, primarily targeting machine learning models. Current research focuses on understanding and mitigating these attacks in various contexts, including object detection (using techniques like naive attacks and dual-source blending within self-supervised learning) and video frame interpolation (employing asymmetric blending modules to improve image quality while potentially being vulnerable to such attacks). The significance lies in the need to enhance the robustness of machine learning systems against these sophisticated attacks, which can compromise the reliability and security of applications ranging from autonomous driving to data security systems.