TruthLens: Visual Grounding for Universal DeepFake Reasoning

• URL: http://arxiv.org/abs/2503.15867v3
• Date: Wed, 03 Sep 2025 01:51:37 GMT
• Title: TruthLens: Visual Grounding for Universal DeepFake Reasoning
• Authors: Rohit Kundu, Shan Jia, Vishal Mohanty, Athula Balachandran, Amit K. Roy-Chowdhury,
• Abstract summary: TruthLens is a novel, unified, and highly generalizable framework that goes beyond traditional binary classification.<n>By unifying high-level scene understanding with fine-grained region grounding, TruthLens delivers transparent DeepFake forensics.
• Score: 22.219182461279214
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Detecting DeepFakes has become a crucial research area as the widespread use of AI image generators enables the effortless creation of face-manipulated and fully synthetic content, while existing methods are often limited to binary classification (real vs. fake) and lack interpretability. To address these challenges, we propose TruthLens, a novel, unified, and highly generalizable framework that goes beyond traditional binary classification, providing detailed, textual reasoning for its predictions. Distinct from conventional methods, TruthLens performs MLLM grounding. TruthLens uses a task-driven representation integration strategy that unites global semantic context from a multimodal large language model (MLLM) with region-specific forensic cues through explicit cross-modal adaptation of a vision-only model. This enables nuanced, region-grounded reasoning for both face-manipulated and fully synthetic content, and supports fine-grained queries such as "Does the eyes/nose/mouth look real or fake?"- capabilities beyond pretrained MLLMs alone. Extensive experiments across diverse datasets demonstrate that TruthLens sets a new benchmark in both forensic interpretability and detection accuracy, generalizing to seen and unseen manipulations alike. By unifying high-level scene understanding with fine-grained region grounding, TruthLens delivers transparent DeepFake forensics, bridging a critical gap in the literature.

Related papers

• INSIGHT: An Interpretable Neural Vision-Language Framework for Reasoning of Generative Artifacts [0.0]
  Current forensic systems degrade sharply under real-world conditions.<n>Most detectors operate as opaques, offering little insight into why an image is flagged as synthetic.<n>We introduce INSIGHT, a unified framework for robust detection and transparent explanation of AI-generated images.
  arXiv  Detail & Related papers  (2025-11-27T11:43:50Z)
• Connecting the Dots: Training-Free Visual Grounding via Agentic Reasoning [63.109585527799005]
  GroundingAgent is a visual grounding framework that operates without task-specific fine-tuning.<n>It achieves an average zero-shot grounding accuracy of 65.1 % on widely-used benchmarks.<n>It also offers strong interpretability, transparently illustrating each reasoning step.
  arXiv  Detail & Related papers  (2025-11-24T03:11:08Z)
• Seeing Before Reasoning: A Unified Framework for Generalizable and Explainable Fake Image Detection [58.82268659497348]
  We argue that the root of this failure lies in a fundamental mismatch: MLLMs are asked to reason about fakes before they can truly see them.<n>We propose Forensic-Chat, a generalizable, explainable, and still-conversational assistant for fake image detection.
  arXiv  Detail & Related papers  (2025-09-29T20:59:19Z)
• Veritas: Generalizable Deepfake Detection via Pattern-Aware Reasoning [45.99344620383706]
  We introduce HydraFake, a dataset that simulates real-world challenges with hierarchical generalization testing.<n>Specifically, HydraFake involves diversified deepfake techniques and in-the-wild forgeries, along with rigorous training and evaluation protocol.<n>We propose Veritas, a multi-modal large language model (MLLM) based deepfake detector.
  arXiv  Detail & Related papers  (2025-08-28T17:53:05Z)
• FakeScope: Large Multimodal Expert Model for Transparent AI-Generated Image Forensics [66.14786900470158]
  We propose FakeScope, an expert multimodal model (LMM) tailored for AI-generated image forensics. FakeScope identifies AI-synthetic images with high accuracy and provides rich, interpretable, and query-driven forensic insights. FakeScope achieves state-of-the-art performance in both closed-ended and open-ended forensic scenarios.
  arXiv  Detail & Related papers  (2025-03-31T16:12:48Z)
• TruthLens:A Training-Free Paradigm for DeepFake Detection [4.64982780843177]
  We introduce TruthLens, a training-free framework that reimagines deepfake detection as a visual question-answering (VQA) task.<n>TruthLens utilizes state-of-the-art large vision-language models (LVLMs) to observe and describe visual artifacts.<n>By adopting a multimodal approach, TruthLens seamlessly integrates visual and semantic reasoning to not only classify images as real or fake but also provide interpretable explanations.
  arXiv  Detail & Related papers  (2025-03-19T15:41:32Z)
• Spot the Fake: Large Multimodal Model-Based Synthetic Image Detection with Artifact Explanation [15.442558725312976]
  We introduce FakeVLM, a specialized large multimodal model for both general synthetic image and DeepFake detection tasks.<n>FakeVLM excels in distinguishing real from fake images and provides clear, natural language explanations for image artifacts.<n>We present FakeClue, a comprehensive dataset containing over 100,000 images across seven categories, annotated with fine-grained artifact clues in natural language.
  arXiv  Detail & Related papers  (2025-03-19T05:14:44Z)
• HFMF: Hierarchical Fusion Meets Multi-Stream Models for Deepfake Detection [4.908389661988192]
  HFMF is a comprehensive two-stage deepfake detection framework.<n>It integrates vision Transformers and convolutional nets through a hierarchical feature fusion mechanism.<n>We demonstrate that our architecture achieves superior performance across diverse dataset benchmarks.
  arXiv  Detail & Related papers  (2025-01-10T00:20:29Z)
• Look Ma, No Ground Truth! Ground-Truth-Free Tuning of Structure from Motion and Visual SLAM [64.57742015099531]
  Evaluation is critical to both developing and tuning Structure from Motion (SfM) and Visual SLAM (VSLAM) systems.<n>This dependency on ground truth restricts SfM and SLAM applications across diverse environments.<n>We propose a novel ground-truth-free (GTF) evaluation methodology that eliminates the need for geometric ground truth.
  arXiv  Detail & Related papers  (2024-12-02T04:40:03Z)
• Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models [68.90917438865078]
  Deepfake techniques for facial synthesis and editing pose serious risks for generative models.<n>In this paper, we investigate how detection performance varies across model backbones, types, and datasets.<n>We introduce Contrastive Blur, which enhances performance on facial images, and MINDER, which addresses noise type bias, balancing performance across domains.
  arXiv  Detail & Related papers  (2024-11-28T13:04:45Z)
• DiffusionFake: Enhancing Generalization in Deepfake Detection via Guided Stable Diffusion [94.46904504076124]
  Deepfake technology has made face swapping highly realistic, raising concerns about the malicious use of fabricated facial content. Existing methods often struggle to generalize to unseen domains due to the diverse nature of facial manipulations. We introduce DiffusionFake, a novel framework that reverses the generative process of face forgeries to enhance the generalization of detection models.
  arXiv  Detail & Related papers  (2024-10-06T06:22:43Z)
• MFCLIP: Multi-modal Fine-grained CLIP for Generalizable Diffusion Face Forgery Detection [64.29452783056253]
  The rapid development of photo-realistic face generation methods has raised significant concerns in society and academia.<n>Although existing approaches mainly capture face forgery patterns using image modality, other modalities like fine-grained noises and texts are not fully explored.<n>We propose a novel multi-modal fine-grained CLIP (MFCLIP) model, which mines comprehensive and fine-grained forgery traces across image-noise modalities.
  arXiv  Detail & Related papers  (2024-09-15T13:08:59Z)
• On the Out-Of-Distribution Generalization of Multimodal Large Language Models [24.431960338495184]
  We investigate the generalization boundaries of current Multimodal Large Language Models (MLLMs) We evaluate their zero-shot generalization across synthetic images, real-world distributional shifts, and specialized datasets like medical and molecular imagery. We show that in-context learning can significantly enhance MLLMs' generalization, opening new avenues for overcoming generalization barriers.
  arXiv  Detail & Related papers  (2024-02-09T18:21:51Z)
• Sparsity-Guided Holistic Explanation for LLMs with Interpretable Inference-Time Intervention [53.896974148579346]
  Large Language Models (LLMs) have achieved unprecedented breakthroughs in various natural language processing domains. The enigmatic black-box'' nature of LLMs remains a significant challenge for interpretability, hampering transparent and accountable applications. We propose a novel methodology anchored in sparsity-guided techniques, aiming to provide a holistic interpretation of LLMs.
  arXiv  Detail & Related papers  (2023-12-22T19:55:58Z)
• Towards General Visual-Linguistic Face Forgery Detection [95.73987327101143]
  Deepfakes are realistic face manipulations that can pose serious threats to security, privacy, and trust. Existing methods mostly treat this task as binary classification, which uses digital labels or mask signals to train the detection model. We propose a novel paradigm named Visual-Linguistic Face Forgery Detection(VLFFD), which uses fine-grained sentence-level prompts as the annotation.
  arXiv  Detail & Related papers  (2023-07-31T10:22:33Z)
• Parents and Children: Distinguishing Multimodal DeepFakes from Natural Images [60.34381768479834]
  Recent advancements in diffusion models have enabled the generation of realistic deepfakes from textual prompts in natural language. We pioneer a systematic study on deepfake detection generated by state-of-the-art diffusion models.
  arXiv  Detail & Related papers  (2023-04-02T10:25:09Z)
• Real Face Foundation Representation Learning for Generalized Deepfake Detection [74.4691295738097]
  The emergence of deepfake technologies has become a matter of social concern as they pose threats to individual privacy and public security. It is almost impossible to collect sufficient representative fake faces, and it is hard for existing detectors to generalize to all types of manipulation. We propose Real Face Foundation Representation Learning (RFFR), which aims to learn a general representation from large-scale real face datasets.
  arXiv  Detail & Related papers  (2023-03-15T08:27:56Z)
• Voice-Face Homogeneity Tells Deepfake [56.334968246631725]
  Existing detection approaches contribute to exploring the specific artifacts in deepfake videos. We propose to perform the deepfake detection from an unexplored voice-face matching view. Our model obtains significantly improved performance as compared to other state-of-the-art competitors.
  arXiv  Detail & Related papers  (2022-03-04T09:08:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.