Trustworthy Image Authentication using Forensic Knowledge Graphs

• URL: http://arxiv.org/abs/2606.23917v1
• Date: Mon, 22 Jun 2026 20:29:21 GMT
• Title: Trustworthy Image Authentication using Forensic Knowledge Graphs
• Abstract summary: We propose Forensic Knowledge Graphs (FKGs), a unified framework that integrates forensic evidence extraction, structured reasoning, and human-interpretable explanation.<n>Our FKG structure encodes forensic traces along with their causal dependencies and links to scene content.<n>We present FKG-50K, a dataset of 50,000 realistic forgeries with ground-truth FKGs.
• Score: 17.042589196506135
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Advances in generative AI have made image falsification highly realistic, demanding trustworthy authentication systems. Existing forensic detectors can target certain forgery types but lack interpretability, while vision-language models (VLMs) provide explanations but cannot exploit forensic traces for reliable detection. We propose Forensic Knowledge Graphs (FKGs), a unified framework that integrates forensic evidence extraction, structured reasoning, and human-interpretable explanation. Our FKG structure encodes forensic traces along with their causal dependencies and links to scene content. To generate accurate FKGs, we introduce a novel forensic authentication network and an Iterative Context Refinement strategy that guides VLMs to produce faithful, grounded explanations. We also present FKG-50K, a dataset of 50,000 realistic forgeries with ground-truth FKGs. Experiments demonstrate that FKG outperforms both forensic detectors and VLMs in detection, forgery identification and localization, and forensic justification.

Related papers

• Defake-o3: From Speculative Rationales to Verifiable Evidence for Explainable AIGI Detection [8.599317072232198]
  We present Defake-o3, an AIGI detector that moves from speculative rationales to verifiable evidence.<n>It combines interactive visual search with verifier-guided evidence alignment.<n>Defake-o3 improves both detection accuracy and explanation quality.
  arXiv  Detail & Related papers  (2026-08-17T08:30:32Z)
• ForensicsTok: Forensics-Guided Tokenized Modeling for Image Tampering Localization [31.83661814822312]
  We propose ForensicsTok, which reformulates image manipulation localization as an autoregressive sequence generation task.<n>ForensicsTok directly generates spatially grounded token sequences, enabling precise mask prediction without intermediary supervision.
  arXiv  Detail & Related papers  (2026-06-23T13:06:18Z)
• Deep Residual Injection for Full-Spectrum Forensic Signal Perception in Multimodal Large Language Models [51.099179436350376]
  Multimodal large language models (MLLMs) have been increasingly adopted in forensics for their robust semantic understanding.<n>We propose Deep Visual Residual MLLM (Deep-VRM) to preserve early semantic processing while injecting artifact-specific visual signals as a residual path into an intermediate layer.<n>This enables later layers to jointly model semantic reasoning and signal-level forensic cues, and surprisingly, the model learns to adaptively leverage different levels of forensic signals depending on the input.
  arXiv  Detail & Related papers  (2026-06-14T16:05:45Z)
• ForensicConcept: Transferable Forensic Concepts for AIGI Detection [81.29606528968698]
  We propose ForensicConcept, a framework that extracts explicit forensic concepts from detectors and enables their transfer across backbones.<n>Our method localizes decision-critical patches via Transformer attribution, clusters them into a compact concept codebook, and uses a concept-aligned projection to produce auditable evidence readouts.
  arXiv  Detail & Related papers  (2026-06-05T08:25:34Z)
• Attribution-Guided Multimodal Deepfake Detection via Cross-Modal Forensic Fingerprints [4.598350938853635]
  We argue that a detector incapable of identifying how a video was forged is likely learning the wrong signal.<n>Unlike binary detection, attribution-guided learning imposes a stronger geometric constraint on the shared embedding space.<n>We propose the Attribution-Guided Multimodal Deepfake Detection framework, which jointly learns to detect and attribute manipulation.
  arXiv  Detail & Related papers  (2026-04-29T09:11:13Z)
• Evaluating the Reliability of Digital Forensic Evidence Discovered by Large Language Model: A Case Study [1.7102309907119588]
  This paper proposes a structured framework that automates forensic artifact extraction, refines data through large language models (LLMs) analysis, and validates results using a Digital Forensic Knowledge Graph (DFKG)<n> evaluated on a 13 GB forensic image dataset containing 61 applications, 2,864 databases, and 5,870 tables.<n>Case study shows framework's effectiveness, achieving over 95 percent accuracy in artifact extraction, strong support of chain-of-custody adherence, and robust contextual consistency in forensic relationships.
  arXiv  Detail & Related papers  (2026-02-22T18:20:49Z)
• REVEAL: Reasoning-enhanced Forensic Evidence Analysis for Explainable AI-generated Image Detection [30.963994372913092]
  We introduce textbfREVEAL-Bench, the first reasoning-enhanced multimodal benchmark for AI-generated image detection.<n>Our framework integrates detection with a novel expert-grounded reinforcement learning.<n> REVEAL significantly enhances detection accuracy, explanation fidelity, and robust cross-model generalization.
  arXiv  Detail & Related papers  (2025-11-28T13:11:08Z)
• Semantic Visual Anomaly Detection and Reasoning in AI-Generated Images [96.43608872116347]
  AnomReason is a large-scale benchmark with structured annotations as quadruple textbfAnomAgent<n>AnomReason and AnomAgent serve as a foundation for measuring and improving the semantic plausibility of AI-generated images.
  arXiv  Detail & Related papers  (2025-10-11T14:09:24Z)
• Propose and Rectify: A Forensics-Driven MLLM Framework for Image Manipulation Localization [49.71303998618939]
  This paper presents a novel Propose-Rectify framework that bridges semantic reasoning with forensic-specific analysis.<n>Our framework ensures that initial semantic proposals are systematically validated and enhanced through concrete technical evidence, resulting in comprehensive detection accuracy and localization precision.
  arXiv  Detail & Related papers  (2025-08-25T12:43:53Z)
• Deepfake Forensic Analysis: Source Dataset Attribution and Legal Implications of Synthetic Media Manipulation [5.764826667785188]
  Synthetic media generated by Generative Adrial Networks (GANs) pose challenges in verifying authenticity and tracing dataset origins.<n>This paper introduces a novel forensic framework for identifying the training dataset (e.g., CelebA or FFHQ) of GAN-generated images through interpretable feature analysis.
  arXiv  Detail & Related papers  (2025-05-16T10:47:18Z)
• 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)
• Forensicability Assessment of Questioned Images in Recapturing Detection [78.45849869266834]
  We propose a forensicability assessment network to quantify the forensicability of the questioned samples. The low-forensicability samples are rejected before the actual recapturing detection process to improve the efficiency of recapturing systems. We integrate the trained FANet with practical recapturing detection schemes in face anti-spoofing and recaptured document detection tasks.
  arXiv  Detail & Related papers  (2022-09-05T12:26:01Z)
• A Principled Design of Image Representation: Towards Forensic Tasks [75.40968680537544]
  We investigate the forensic-oriented image representation as a distinct problem, from the perspectives of theory, implementation, and application. At the theoretical level, we propose a new representation framework for forensics, called Dense Invariant Representation (DIR), which is characterized by stable description with mathematical guarantees. We demonstrate the above arguments on the dense-domain pattern detection and matching experiments, providing comparison results with state-of-the-art descriptors.
  arXiv  Detail & Related papers  (2022-03-02T07:46:52Z)

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.