Image Backdoor Attack

Image backdoor attacks exploit vulnerabilities in machine learning models by embedding hidden triggers that cause the model to misclassify inputs in a specific way, even without modifying the input image itself. Current research focuses on developing increasingly sophisticated attack methods, including those that leverage data labeling errors, inaudible audio modifications, or imperceptible image alterations, targeting various model types from image classifiers to generative AI and even video recognition systems. These attacks highlight significant security risks in machine learning systems, particularly those relying on outsourced data or pre-trained models, underscoring the need for robust defenses and improved model training practices. The development of effective detection and mitigation strategies is crucial for ensuring the reliability and trustworthiness of AI systems across diverse applications.