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

> This article introduces Deep-VRM, a paper accepted by ICML 2026. The technology enhances the forensic signal perception capability of multimodal large language models (MLLMs) through a deep residual injection mechanism, implements two-stage training based on Qwen2.5-VL, and provides new ideas for AI-generated content detection and multimedia forensics.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-05-25T12:21:33.000Z
- 最近活动: 2026-05-25T13:18:32.602Z
- 热度: 152.1
- 关键词: 多模态大语言模型, 多媒体取证, 深度残差注入, AI生成内容检测, 深度伪造识别, Qwen2.5-VL, ICML 2026, 计算机视觉, 机器学习安全
- 页面链接: https://www.zingnex.cn/en/forum/thread/deep-vrm
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## Deep-VRM Technology Guide: Full-Spectrum Forensic Signal Perception Scheme for Multimodal Large Language Models

This article introduces Deep-VRM, a paper accepted by ICML 2026. The technology enhances the forensic signal perception capability of multimodal large language models (MLLMs) through a deep residual injection mechanism, implements two-stage training based on Qwen2.5-VL, and provides new ideas for AI-generated content detection and multimedia forensics.

Original Author/Maintainer: KQL11
Source Platform: GitHub
Original Title: Deep-VRM: Deep Residual Injection for Full-Spectrum Forensic Signal Perception in Multimodal Large Language Models
Original Link: https://github.com/KQL1/Deep-VRM
Source Publication Time/Update Time: 2026-05-25

## Research Background: Multimedia Forensic Challenges Brought by Generative AI

With the rapid development of generative AI technology, multimodal large language models (MLLMs) perform excellently in tasks like image understanding, but the demand to distinguish between real and AI-generated content is increasingly urgent. The proliferation of deepfake technology has made multimedia forensics a focus.

Traditional forensic methods are designed for specific tampering techniques and struggle to cope with rapidly iterating generative models; existing MLLMs excel at high-level semantic understanding but lack sensitivity to subtle forensic clues hidden in images (such as compression traces, noise patterns, generation artifacts, etc.).

## Core of Deep-VRM Technology: Deep Residual Injection and Full-Spectrum Perception

Deep-VRM enables MLLMs to have full-spectrum forensic signal perception capability through a deep residual injection mechanism:
- Full-spectrum perception: Captures multi-band clues such as low-frequency (overall structural anomalies), medium-frequency (unnatural texture boundaries), and high-frequency (abnormal noise distribution)

Two-stage training strategy based on Qwen2.5-VL:
1. Base model training: Uses standard visual instruction fine-tuning data to establish visual-language alignment capability
2. Residual injection training: Introduces the DeepVRM module, injects low-level visual features via residual connections, including residual feature extraction, multi-scale fusion, and adaptive injection (gating mechanism controls intensity)

## Experimental Design and Evaluation Ideas

Inferred from the code repository structure: Adopts a modular architecture, supporting efficient training with the ms-swift framework.

Evaluation will cover the following tasks:
- Generated image detection: Distinguish between real photos and AI-generated images
- Tampering detection: Locate tampered areas like splicing and copy-paste
- Deepfake detection: Identify traces of face-swapped videos/voice forgery
- Multimodal consistency verification: Detect consistency between images and text descriptions

The full-spectrum perception feature of Deep-VRM gives it potential advantages in fine-grained analysis scenarios.

## Technical Implementation Details: Modular Design and Training Support

The project provides complete training and inference scripts:
- `run_Stage1.sh`: First-stage training script
- `run_Stage2.sh`: Second-stage residual injection training script
- `Models/DeepVRM/`: Core model implementation
- `ms-swift/`: Swift training framework integration

Supports parameter-efficient fine-tuning methods (e.g., LoRA, QLoRA), and the modular design facilitates reproduction and extension.

## Research Limitations and Future Directions

**Limitations**:
1. Training data and model weights have not been made public yet
2. Cross-domain generalization ability (unseen generation/tampering techniques) needs verification
3. Computational overhead caused by residual injection needs optimization

**Future Directions**:
- Explore lightweight residual injection architectures
- Extend to video forensics scenarios
- Develop interpretability tools
- Establish a unified benchmark testing platform

## Summary: Significance and Insights of Deep-VRM

Deep-VRM combines fine-grained forensic signal perception with powerful semantic understanding, opening up new directions for AI-generated content detection and multimedia forensics.

It provides technical references for AI security, content moderation, and digital forensics fields. The open-source code contributes a reproducible foundation to the community, and we look forward to the complete version driving the development of the field.