Exploring Multi-view Pixel Contrast for General and Robust Image Forgery Localization

• URL: http://arxiv.org/abs/2406.13565v1
• Date: Wed, 19 Jun 2024 13:51:52 GMT
• Title: Exploring Multi-view Pixel Contrast for General and Robust Image Forgery Localization
• Authors: Zijie Lou, Gang Cao, Kun Guo, Haochen Zhu, Lifang Yu,
• Abstract summary: We propose a Multi-view Pixel-wise Contrastive algorithm (MPC) for image forgery localization. Specifically, we first pre-train the backbone network with the supervised contrastive loss. Then the localization head is fine-tuned using the cross-entropy loss, resulting in a better pixel localizer.
• Score: 4.8454936010479335
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image forgery localization, which aims to segment tampered regions in an image, is a fundamental yet challenging digital forensic task. While some deep learning-based forensic methods have achieved impressive results, they directly learn pixel-to-label mappings without fully exploiting the relationship between pixels in the feature space. To address such deficiency, we propose a Multi-view Pixel-wise Contrastive algorithm (MPC) for image forgery localization. Specifically, we first pre-train the backbone network with the supervised contrastive loss to model pixel relationships from the perspectives of within-image, cross-scale and cross-modality. That is aimed at increasing intra-class compactness and inter-class separability. Then the localization head is fine-tuned using the cross-entropy loss, resulting in a better pixel localizer. The MPC is trained on three different scale training datasets to make a comprehensive and fair comparison with existing image forgery localization algorithms. Extensive experiments on the small, medium and large scale training datasets show that the proposed MPC achieves higher generalization performance and robustness against post-processing than the state-of-the-arts. Code will be available at https://github.com/multimediaFor/MPC.

Related papers

• Co-Segmentation without any Pixel-level Supervision with Application to Large-Scale Sketch Classification [3.3104978705632777]
  We propose a novel method for object co-segmentation, i.e. pixel-level localization of a common object in a set of images. The method achieves state-of-the-art performance among methods trained with the same level of supervision. The benefits of the proposed co-segmentation method are further demonstrated in the task of large-scale sketch recognition.
  arXiv  Detail & Related papers  (2024-10-17T14:16:45Z)
• Parameter-Inverted Image Pyramid Networks [49.35689698870247]
  We propose a novel network architecture known as the Inverted Image Pyramid Networks (PIIP) Our core idea is to use models with different parameter sizes to process different resolution levels of the image pyramid. PIIP achieves superior performance in tasks such as object detection, segmentation, and image classification.
  arXiv  Detail & Related papers  (2024-06-06T17:59:10Z)
• Learning Invariant Inter-pixel Correlations for Superpixel Generation [12.605604620139497]
  Learnable features exhibit constrained discriminative capability, resulting in unsatisfactory pixel grouping performance. We propose the Content Disentangle Superpixel algorithm to selectively separate the invariant inter-pixel correlations and statistical properties. The experimental results on four benchmark datasets demonstrate the superiority of our approach to existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-02-28T09:46:56Z)
• Pixel-Inconsistency Modeling for Image Manipulation Localization [63.54342601757723]
  Digital image forensics plays a crucial role in image authentication and manipulation localization. This paper presents a generalized and robust manipulation localization model through the analysis of pixel inconsistency artifacts. Experiments show that our method successfully extracts inherent pixel-inconsistency forgery fingerprints.
  arXiv  Detail & Related papers  (2023-09-30T02:54:51Z)
• Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training [51.16994853817024]
  This work focuses on designing an effective pre-training framework for 3D radiology images. We introduce Disruptive Autoencoders, a pre-training framework that attempts to reconstruct the original image from disruptions created by a combination of local masking and low-level perturbations. The proposed pre-training framework is tested across multiple downstream tasks and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-07-31T17:59:42Z)
• Multi-spectral Class Center Network for Face Manipulation Detection and Localization [52.569170436393165]
  We propose a novel Multi-Spectral Class Center Network (MSCCNet) for face manipulation detection and localization. Based on the features of different frequency bands, the MSCC module collects multi-spectral class centers and computes pixel-to-class relations. Applying multi-spectral class-level representations suppresses the semantic information of the visual concepts which is insensitive to manipulated regions of forgery images.
  arXiv  Detail & Related papers  (2023-05-18T08:09:20Z)
• Pixel Relationships-based Regularizer for Retinal Vessel Image Segmentation [4.3251090426112695]
  This study presents regularizers to give the pixel neighbor relationship information to the learning process. Experiments show that our scheme successfully captures pixel neighbor relations and improves the performance of the convolutional neural network.
  arXiv  Detail & Related papers  (2022-12-28T07:35:20Z)
• LocalTrans: A Multiscale Local Transformer Network for Cross-Resolution Homography Estimation [52.63874513999119]
  Cross-resolution image alignment is a key problem in multiscale giga photography. Existing deep homography methods neglecting the explicit formulation of correspondences between them, which leads to degraded accuracy in cross-resolution challenges. We propose a local transformer network embedded within a multiscale structure to explicitly learn correspondences between the multimodal inputs.
  arXiv  Detail & Related papers  (2021-06-08T02:51:45Z)
• Image Fine-grained Inpainting [89.17316318927621]
  We present a one-stage model that utilizes dense combinations of dilated convolutions to obtain larger and more effective receptive fields. To better train this efficient generator, except for frequently-used VGG feature matching loss, we design a novel self-guided regression loss. We also employ a discriminator with local and global branches to ensure local-global contents consistency.
  arXiv  Detail & Related papers  (2020-02-07T03:45:25Z)

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.