3 papers
cs.CV2026
When Recovery Matters: The Blind Spot of Surrogate Privacy in MLLM Editing
Siyuan Xu, Yibing Liu, Peilin Chen +3
Multimodal Large Language Models (MLLMs) enable flexible instruction-driven image editing, but privacy risks arise when user images expose diverse and user-specific private content…
cs.CV2025
When Privacy Meets Recovery: The Overlooked Half of Surrogate-Driven Privacy Preservation for MLLM Editing
Siyuan Xu, Yibing Liu, Peilin Chen +3
Privacy leakage in Multimodal Large Language Models (MLLMs) has long been an intractable problem. Existing studies, though effectively obscure private information in MLLMs, often o…
cs.CV2025
Leveraging Diffusion Knowledge for Generative Image Compression with Fractal Frequency-Aware Band Learning
Lingyu Zhu, Xiangrui Zeng, Bolin Chen +3
By optimizing the rate-distortion-realism trade-off, generative image compression approaches produce detailed, realistic images instead of the only sharp-looking reconstructions pr…