In the era of social media, the proliferation of fake news has created an urgent need for more effective detection methods, particularly for multimodal content. The task of identifying fake news is highly challenging, as it requires broad background knowledge and understanding across various domains. Existing detection methods primarily rely on neural networks to learn latent feature representations, resulting in black-box classifications with limited real-world understanding. To address these limitations, we propose a novel approach that leverages Multimodal Large Language Models (MLLMs) for fake news detection. Our method introduces adversarial reasoning through debates from opposing perspectives. By harnessing the powerful capabilities of MLLMs in text generation and cross-modal reasoning, we guide these models to engage in multimodal debates, generating adversarial arguments based on contradictory evidence from both sides of the issue. We then utilize these arguments to learn reasonable thinking patterns, enabling better multimodal fusion and fine-tuning. This process effectively positions our model as a debate referee for adversarial inference. Extensive experiments conducted on four fake news detection datasets demonstrate that our proposed method significantly outperforms state-of-the-art approaches.

With the increasing scale of training data for Multimodal Large Language Models (MLLMs) and the lack of data details, there is growing concern about privacy breaches and data security issues. Under black-box access, exploring effective Membership Inference Attacks (MIA) has garnered increasing attention. In real-world applications, where most samples are non-members, the issue of non-members being over-represented in the data manifold, leading to misclassification as member samples, becomes more prominent. This has motivated recent work to focus on developing effective difficulty calibration strategies, producing promising results. However, these methods only consider text-only input during calibration, and their effectiveness is diminished when migrated to MLLMs due to the presence of visual embeddings. To address the above problem, we propose PC-MMIA, focusing on visual instruction fine-tuning data. PC-MMIA is based on the idea that tokens located in poorly generalized local manifolds can better reflect traces of member samples that have been trained. By employing bidirectional perturbation of image embeddings to capture tokens critical to MIA and assigning them different weights, we achieve difficulty calibration. Experimental results demonstrate that our proposed method surpasses existing methods.