Boosting Adversarial Transferability via Ensemble Non-Attention

The development of NAMEA represents a significant advancement in adversarial attack strategies, particularly in improving transferability across diverse model architectures. This aligns with ongoing research efforts, such as those highlighted in 'CertMask,' which focuses on defending against adversarial patch attacks. Both studies underscore the importance of addressing vulnerabilities in deep learning models. Furthermore, the exploration of gradient optimization techniques, as seen in 'Scaling Textual Gradients via Sampling-Based Momentum,' complements the findings of NAMEA by emphasizing the need for effective prompt engineering and model robustness in adversarial contexts.