Deep Residual Injection for Full-Spectrum Forensic Signal Perception in Multimodal Large Language Models

The article introduces the Deep Visual Residual MLLM (Deep-VRM), a novel approach aimed at enhancing forensic signal perception in multimodal large language models (MLLMs). It employs a unique architecture that injects artifact-specific visual signals into intermediate layers while preserving early semantic processing, allowing the model to effectively combine semantic reasoning with forensic cues. This method achieves state-of-the-art performance across various benchmarks, making it significant for practitioners focused on improving detection capabilities in forensic applications of AI-generated content.