The field of multimodal research is moving towards developing more sophisticated models that can effectively integrate and process multiple forms of data, such as images and text. This is driven by the need for more accurate and robust models that can perform tasks like object segmentation, scene understanding, and safety-aware reasoning. One notable direction is the development of models that can protect sensitive visual information and preserve privacy, while still allowing for effective scene understanding and object recognition. Another area of focus is the creation of more robust models that can withstand adversarial attacks and jailbreak attempts, which is critical for ensuring the safety and security of multimodal systems. Noteworthy papers in this area include:

• A novel privacy-preserving framework that leverages feedback-based reinforcement learning and vision-language models to protect sensitive visual information, which achieved significant improvements in both privacy protection and textual quality.
• A multimodal bidirectional attack strategy that introduces learnable proxy textual embedding perturbation and jointly performs visual-aligned optimization on the image modality and textual-adversarial optimization on the textual modality, demonstrating superior effectiveness compared to existing methods.
• A black-box jailbreak attack framework that decomposes malicious prompts into semantically benign visual and textual fragments, which supports adjustable reasoning complexity and requires significantly fewer queries than prior attacks.
• A large-scale multimodal dataset with chain-of-thought reasoning for harmful meme detection, which fills critical gaps in current datasets and provides a solid foundation for enhancing harmful meme detection.
• A policy-grounded multimodal alignment dataset tailored to bridge the gap in safety alignment for vision-language models, which substantially improves robustness against both textual and vision-language jailbreak attacks.