Enhancing Adversarial Transferability by Balancing Exploration and Exploitation with Gradient-Guided Sampling

A recent study explores how balancing exploration and exploitation strategies can enhance the transferability of adversarial attacks on deep neural networks, addressing a key challenge in the field of adversarial attack transferability (F1). The research focuses on resolving the dilemma between maximizing the potency of attacks and improving their generalization across different model architectures (F2). By employing gradient-guided sampling, the study demonstrates that this balance leads to more effective adversarial attacks that transfer better between models (A1). These findings suggest potential improvements in the robustness of AI systems by providing deeper insights into adversarial vulnerabilities (A2). The work contributes to ongoing efforts to understand and mitigate risks associated with adversarial attacks in machine learning. Overall, the study offers promising directions for enhancing AI security through refined attack strategies.