ETA: Evaluating Then Aligning Safety of Vision Language Models at Inference Time
<|reference_start|>ETA: Evaluating Then Aligning Safety of Vision Language Models at Inference Time: Vision Language Models (VLMs) have become essential backbones for multimodal intelligence, yet significant safety challenges limit their real-world application. While textual inputs are often effectively safeguarded, adversarial visual inputs can easily bypass VLM defense mechanisms. Existing defense methods are either resource-intensive, requiring substantial data and compute, or fail to simultaneously ensure safety and usefulness in responses. To address these limitations, we propose a novel two-phase inference-time alignment framework, Evaluating Then Aligning (ETA): 1) Evaluating input visual contents and output responses to establish a robust safety awareness in multimodal settings, and 2) Aligning unsafe behaviors at both shallow and deep levels by conditioning the VLMs' generative distribution with an interference prefix and performing sentence-level best-of-N to search the most harmless and helpful generation paths. Extensive experiments show that ETA outperforms baseline methods in terms of harmlessness, helpfulness, and efficiency, reducing the unsafe rate by 87.5% in cross-modality attacks and achieving 96.6% win-ties in GPT-4 helpfulness evaluation. The code is publicly available at https://github.com/DripNowhy/ETA.<|reference_end|>
@article{ding2024eta:,
    title={ETA: Evaluating Then Aligning Safety of Vision Language Models at
  Inference Time},
    author={Yi Ding, Bolian Li, Ruqi Zhang},
    journal={arXiv preprint arXiv:2410.06625},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.06625},
    primaryClass={cs.CV cs.CL cs.LG}
}