TruFor: Leveraging all-round clues for trustworthy image forgery detection and localization

• URL: http://arxiv.org/abs/2212.10957v3
• Date: Thu, 25 May 2023 20:59:59 GMT
• Title: TruFor: Leveraging all-round clues for trustworthy image forgery detection and localization
• Authors: Fabrizio Guillaro and Davide Cozzolino and Avneesh Sud and Nicholas Dufour and Luisa Verdoliva
• Abstract summary: TruFor is a forensic framework that can be applied to a large variety of image manipulation methods. We rely on the extraction of both high-level and low-level traces through a transformer-based fusion architecture. Our method is able to reliably detect and localize both cheapfakes and deepfakes manipulations outperforming state-of-the-art works.
• Score: 17.270110456445806
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we present TruFor, a forensic framework that can be applied to a large variety of image manipulation methods, from classic cheapfakes to more recent manipulations based on deep learning. We rely on the extraction of both high-level and low-level traces through a transformer-based fusion architecture that combines the RGB image and a learned noise-sensitive fingerprint. The latter learns to embed the artifacts related to the camera internal and external processing by training only on real data in a self-supervised manner. Forgeries are detected as deviations from the expected regular pattern that characterizes each pristine image. Looking for anomalies makes the approach able to robustly detect a variety of local manipulations, ensuring generalization. In addition to a pixel-level localization map and a whole-image integrity score, our approach outputs a reliability map that highlights areas where localization predictions may be error-prone. This is particularly important in forensic applications in order to reduce false alarms and allow for a large scale analysis. Extensive experiments on several datasets show that our method is able to reliably detect and localize both cheapfakes and deepfakes manipulations outperforming state-of-the-art works. Code is publicly available at https://grip-unina.github.io/TruFor/

Related papers

• DeCLIP: Decoding CLIP representations for deepfake localization [4.04729645587678]
  We introduce DeCLIP, a first attempt to leverage large pretrained features for detecting local manipulations. We show that, when combined with a reasonably large convolutional decoder, pretrained self-supervised representations are able to perform localization. Unlike previous work, our approach is able to perform localization on the challenging case of latent diffusion models.
  arXiv  Detail & Related papers  (2024-09-12T17:59:08Z)
• FSBI: Deepfakes Detection with Frequency Enhanced Self-Blended Images [17.707379977847026]
  This paper introduces a Frequency Enhanced Self-Blended Images approach for deepfakes detection. The proposed approach has been evaluated on FF++ and Celeb-DF datasets.
  arXiv  Detail & Related papers  (2024-06-12T20:15:00Z)
• 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)
• Towards Effective Image Manipulation Detection with Proposal Contrastive Learning [61.5469708038966]
  We propose Proposal Contrastive Learning (PCL) for effective image manipulation detection. Our PCL consists of a two-stream architecture by extracting two types of global features from RGB and noise views respectively. Our PCL can be easily adapted to unlabeled data in practice, which can reduce manual labeling costs and promote more generalizable features.
  arXiv  Detail & Related papers  (2022-10-16T13:30:13Z)
• Deep Convolutional Pooling Transformer for Deepfake Detection [54.10864860009834]
  We propose a deep convolutional Transformer to incorporate decisive image features both locally and globally. Specifically, we apply convolutional pooling and re-attention to enrich the extracted features and enhance efficacy. The proposed solution consistently outperforms several state-of-the-art baselines on both within- and cross-dataset experiments.
  arXiv  Detail & Related papers  (2022-09-12T15:05:41Z)
• Delving into Sequential Patches for Deepfake Detection [64.19468088546743]
  Recent advances in face forgery techniques produce nearly untraceable deepfake videos, which could be leveraged with malicious intentions. Previous studies has identified the importance of local low-level cues and temporal information in pursuit to generalize well across deepfake methods. We propose the Local- & Temporal-aware Transformer-based Deepfake Detection framework, which adopts a local-to-global learning protocol.
  arXiv  Detail & Related papers  (2022-07-06T16:46:30Z)
• 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)
• Noise Doesn't Lie: Towards Universal Detection of Deep Inpainting [42.189768203036394]
  We make the first attempt towards universal detection of deep inpainting, where the detection network can generalize well. Our approach outperforms existing detection methods by a large margin and generalizes well to unseen deep inpainting techniques.
  arXiv  Detail & Related papers  (2021-06-03T01:29:29Z)
• Identifying Invariant Texture Violation for Robust Deepfake Detection [17.306386179823576]
  We propose the Invariant Texture Learning framework, which only accesses the published dataset with low visual quality. Our method is based on the prior that the microscopic facial texture of the source face is inevitably violated by the texture transferred from the target person.
  arXiv  Detail & Related papers  (2020-12-19T03:02:15Z)
• Unsupervised Metric Relocalization Using Transform Consistency Loss [66.19479868638925]
  Training networks to perform metric relocalization traditionally requires accurate image correspondences. We propose a self-supervised solution, which exploits a key insight: localizing a query image within a map should yield the same absolute pose, regardless of the reference image used for registration. We evaluate our framework on synthetic and real-world data, showing our approach outperforms other supervised methods when a limited amount of ground-truth information is available.
  arXiv  Detail & Related papers  (2020-11-01T19:24:27Z)
• What makes fake images detectable? Understanding properties that generalize [55.4211069143719]
  Deep networks can still pick up on subtle artifacts in doctored images. We seek to understand what properties of fake images make them detectable. We show a technique to exaggerate these detectable properties.
  arXiv  Detail & Related papers  (2020-08-24T17:50:28Z)

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.