Black-Box Membership Inference Attacks for Video Training Data in Multimodal Large Language Models

The increasing use of video data in training multimodal large language models (MLLMs) raises significant concerns on privacy leakage and copyright violations, highlighting the need for detecting improperly used training videos through membership inference attacks (MIAs). Most existing video MIA methods assess model memorization of key semantic concepts within a video (e.g., the name of a well-known movie character). However, such concepts usually appear repeatedly throughout the training corpus, and memorization of them does not constitute reliable evidence that a specific video was used during training. Besides, while some methods mitigate this limitation by capturing relationships between frames, they require a model logit-accessible setting and are impractical in realistic black-box scenarios. To address these challenges, we propose a black-box MIA framework, named VideoMIA, that can provide reliable evidence of specific video data usage for training MLLMs. The key of our method is to leverage temporal dependencies across video frames to evaluate the model's memorization of sequential dynamics within the video data, which cannot be inferred solely from general world knowledge or individual image data. The results across ten MLLMs and four benchmarks demonstrate that our method consistently achieves superior performance over all baselines in black-box evaluation settings. Code is available in https: //github.com/jinruiwang258/VideoMIA .