Effective Backdoor
Effective backdoor attacks exploit vulnerabilities in machine learning models, surreptitiously manipulating their behavior by injecting malicious data or modifying model architecture. Current research focuses on understanding and mitigating these attacks across various model types, including large vision-language models, embodied AI systems, and federated learning architectures, exploring diverse attack vectors such as data poisoning, trigger injection (including subtle frequency-domain manipulations and even simple rotations), and circuit-level modifications for quantum neural networks. The widespread impact of these attacks on the security and reliability of AI systems, particularly in safety-critical applications like autonomous driving and robotics, necessitates ongoing research into robust defenses and detection methods.