Junhao Dong

Adversarial techniques traditionally seen as threats are now being repurposed by content owners to proactively safeguard their visual assets from misuse.

Evolving adversarial attacks across both text and image modalities can dramatically enhance the transferability and effectiveness of adversarial perturbations in vision-language models.

Collaborative learning among specialized model experts can dramatically enhance adversarial robustness in vision-language models, outperforming traditional fine-tuning methods.

Adversarial training can be made more effective by considering the hierarchical relationships between classes, leading to vision-language models that are more robust to attacks on both specific classes and their broader categories.