Transferable Attack

Transferable attacks aim to create adversarial examples that can fool multiple machine learning models, even those unseen during the attack's design. Current research focuses on improving the transferability of these attacks across various model architectures (including CNNs, LLMs, and clustering algorithms) and data modalities (images, text, audio, and even skeletal data), often employing techniques like generative adversarial networks (GANs), Bayesian optimization, and gradient editing. This research is crucial for assessing the robustness of machine learning systems and developing effective defenses against malicious manipulations in diverse real-world applications, such as autonomous driving and cybersecurity. The overarching goal is to understand and mitigate the vulnerabilities of AI systems to these attacks.