# LaP-Forensics: Latent-Pixel Consistency Guided Multimodal Reasoning for Deepfake Detection

arXiv: https://arxiv.org/abs/2607.25962 · HTML: https://mlsanity.com/paper/2607.25962/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 29 of its 57 references that are in the map.
- First posted on arXiv 28 Jul 2026.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)
- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 29 of 29)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [DIRE for Diffusion-Generated Image Detection](https://mlsanity.com/paper/2303.09295/) (2023)
- [What makes fake images detectable? Understanding properties that generalize](https://mlsanity.com/paper/2008.10588/) (2020)
- [A Sanity Check for AI-generated Image Detection](https://mlsanity.com/paper/2406.19435/) (2024)
- [Detecting GAN generated Fake Images using Co-occurrence Matrices](https://mlsanity.com/paper/1903.06836/) (2019)
- [SIDA: Social Media Image Deepfake Detection, Localization and Explanation with Large Multimodal Model](https://mlsanity.com/paper/2412.04292/) (2024)
- [LEGION: Learning to Ground and Explain for Synthetic Image Detection](https://mlsanity.com/paper/2503.15264/) (2025)
- [D3: Scaling Up Deepfake Detection by Learning from Discrepancy](https://mlsanity.com/paper/2404.04584/) (2024)
- [IMAGGarment: Fine-Grained Garment Generation for Controllable Fashion Design](https://mlsanity.com/paper/2504.13176/) (2025)
- [All Patches Matter, More Patches Better: Enhance AI-Generated Image Detection via Panoptic Patch Learning](https://mlsanity.com/paper/2504.01396/) (2025)
- [OpenSDI: Spotting Diffusion-Generated Images in the Open World](https://mlsanity.com/paper/2503.19653/) (2025)
- [Seeing Before Reasoning: A Unified Framework for Generalizable and Explainable Fake Image Detection](https://mlsanity.com/paper/2509.25502/) (2025)
- [DNA: Uncovering Universal Latent Forgery Knowledge](https://mlsanity.com/paper/2601.22515/) (2026)
- [Where, What, Why: Towards Explainable Driver Attention Prediction](https://mlsanity.com/paper/2506.23088/) (2025)
- [Interpretable and Reliable Detection of AI-Generated Images via Grounded Reasoning in MLLMs](https://mlsanity.com/paper/2506.07045/) (2025)
- [Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples](https://mlsanity.com/paper/2509.25682/) (2025)
- [Beyond Surface Artifacts: Capturing Shared Latent Forgery Knowledge Across Modalities](https://mlsanity.com/paper/2604.07763/) (2026)
- [No Pixel Left Behind: A Detail-Preserving Architecture for Robust High-Resolution AI-Generated Image Detection](https://mlsanity.com/paper/2508.17346/) (2025)
- [ManipShield: A Unified Framework for Image Manipulation Detection, Localization and Explanation](https://mlsanity.com/paper/2511.14259/) (2025)
- [Locate-Then-Examine: Grounded Region Reasoning Improves Detection of AI-Generated Images](https://mlsanity.com/paper/2510.04225/) (2025)
- [A Comprehensive Dataset for Human vs. AI Generated Image Detection](https://mlsanity.com/paper/2601.00553/) (2026)
- [EditTrack: Detecting and Attributing AI-assisted Image Editing](https://mlsanity.com/paper/2510.01173/) (2025)

## Built on by (most cited first, 1 of 1)

- [Beyond Visual Similarity: Entity-Aligned Retrieval for Knowledge-Based Visual Question Answering](https://mlsanity.com/paper/2608.21450/) (2026)

## Abstract (arXiv)

Recent generative models can produce images with few obvious visual artifacts, weakening detectors and explanations that rely only on surface appearance. We present LaP-Forensics, a multimodal framework that augments RGB semantics with reconstruction-based forensic evidence. A frozen Stable Diffusion DDIM inversion-reconstruction model provides a fixed reconstruction reference, and its residual map measures local compatibility with that reference. Independent projectors encode the RGB image and residual map before a structured Where-What-Why model predicts a textual analysis and an artifact mask.Supervised fine-tuning is followed by Group Relative Policy Optimization (GRPO), whose reward combines mask overlap with output-structure and evidence-reference terms. These text-side terms encourage the model to refer to the consistency map but do not constitute a verifier of free-form textual truth. A separate image-level head fuses RGB and DDIM-residual class features. Experiments show cross-generator detection on UniversalFakeDetect and competitive artifact localization on the official SynthScars benchmark. Controlled cue-construction, inversion-horizon, component, reward-term, and counterfactual analyses support the utility of the residual stream under the evaluated settings, while free-form textual faithfulness and reliability under post-processing remain open limitations.