From Detection to Localization: A Unified Forensics Framework for Fully Synthetic and Tampered Images

• URL: http://arxiv.org/abs/2609.02640v1
• Date: Wed, 02 Sep 2026 14:16:44 GMT
• Title: From Detection to Localization: A Unified Forensics Framework for Fully Synthetic and Tampered Images
• Abstract summary: This work extends an existing detector by introducing a unified multiclass framework (real vs. fully generated vs. tampered)<n>In addition to classifying image authenticity, the framework incorporates a segmentation branch to enable pixel-level localization of tampered regions.<n>The proposed approach outperforms selected recent benchmarks, offering an efficient solution with improved classification accuracy and higher IoU scores for the localization task.
• Score: 7.9180538960840385
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid advancement of generative models has significantly worsened the problem of manipulated image detection, as these methods are capable of producing highly realistic forgeries, reinforcing the importance of multimedia forensics. Conventional approaches typically frame image manipulation detection as a binary classification task (real vs. generated), which limits the capability to distinguish and localize different forms of manipulation. To address these constraints, this work extends an existing detector by introducing a unified multiclass framework (real vs. fully generated vs. tampered). In addition to classifying image authenticity, the framework incorporates a segmentation branch to enable pixel-level localization of tampered regions. The proposed approach outperforms selected recent benchmarks, offering an efficient solution with improved classification accuracy and higher IoU scores for the localization task. Find the code at https://github.com/anngal01/From-Detection-to-Localization-A-Unified-Forensics-Framework-for-Fully-S ynthetic-and-Tampered-Images.

Related papers

• FUSED: Forensic-Semantic Mixture-of-Experts for AI Inpainting Detection and Localization [14.056602265859645]
  Diffusion-based inpainting models modify only a localized part of an image.<n>Many AI-image detectors rely on global artifacts and do not localize.<n>We present FUSED, a unified framework for the joint detection and localization of AI-generated inpainting.
  arXiv  Detail & Related papers  (2026-08-28T13:05:43Z)
• Leveraging Hierarchical Image-Text Misalignment for Universal Fake Image Detection [58.927873049646024]
  We show that fake images cannot be properly aligned with corresponding captions compared to real images.<n>We propose a simple yet effective ITEM by leveraging the image-text misalignment in a joint visual-language space as discriminative clues.
  arXiv  Detail & Related papers  (2025-11-01T06:51:14Z)
• UniShield: An Adaptive Multi-Agent Framework for Unified Forgery Image Detection and Localization [20.95924601497973]
  UniShield is a novel unified system capable of detecting and localizing image forgeries across diverse domains.<n>We show that UniShield achieves state-of-the-art results, surpassing both existing unified approaches and domain-specific detectors.
  arXiv  Detail & Related papers  (2025-10-03T16:33:05Z)
• 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)
• Context-Aware Weakly Supervised Image Manipulation Localization with SAM Refinement [52.15627062770557]
  Malicious image manipulation poses societal risks, increasing the importance of effective image manipulation detection methods.<n>Recent approaches in image manipulation detection have largely been driven by fully supervised approaches.<n>We present a novel weakly supervised framework based on a dual-branch Transformer-CNN architecture.
  arXiv  Detail & Related papers  (2025-03-26T07:35:09Z)
• Weakly-supervised deepfake localization in diffusion-generated images [4.548755617115687]
  We propose a weakly-supervised localization problem based on the Xception network as the backbone architecture. We show that the best performing detection method (based on local scores) is less sensitive to the looser supervision than to the mismatch in terms of dataset or generator.
  arXiv  Detail & Related papers  (2023-11-08T10:27:36Z)
• ObjectFormer for Image Manipulation Detection and Localization [118.89882740099137]
  We propose ObjectFormer to detect and localize image manipulations. We extract high-frequency features of the images and combine them with RGB features as multimodal patch embeddings. We conduct extensive experiments on various datasets and the results verify the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2022-03-28T12:27:34Z)
• Region-level Active Learning for Cluttered Scenes [60.93811392293329]
  We introduce a new strategy that subsumes previous Image-level and Object-level approaches into a generalized, Region-level approach. We show that this approach significantly decreases labeling effort and improves rare object search on realistic data with inherent class-imbalance and cluttered scenes.
  arXiv  Detail & Related papers  (2021-08-20T14:02:38Z)
• Re-rank Coarse Classification with Local Region Enhanced Features for Fine-Grained Image Recognition [22.83821575990778]
  We re-rank the TopN classification results by using the local region enhanced embedding features to improve the Top1 accuracy. To learn more effective semantic global features, we design a multi-level loss over an automatically constructed hierarchical category structure. Our method achieves state-of-the-art performance on three benchmarks: CUB-200-2011, Stanford Cars, and FGVC Aircraft.
  arXiv  Detail & Related papers  (2021-02-19T11:30:25Z)
• Instance Localization for Self-supervised Detection Pretraining [68.24102560821623]
  We propose a new self-supervised pretext task, called instance localization. We show that integration of bounding boxes into pretraining promotes better task alignment and architecture alignment for transfer learning. Experimental results demonstrate that our approach yields state-of-the-art transfer learning results for object detection.
  arXiv  Detail & Related papers  (2021-02-16T17:58:57Z)

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.