Hierarchical Fine-Grained Image Forgery Detection and Localization

• URL: http://arxiv.org/abs/2303.17111v1
• Date: Thu, 30 Mar 2023 02:51:52 GMT
• Title: Hierarchical Fine-Grained Image Forgery Detection and Localization
• Authors: Xiao Guo, Xiaohong Liu, Zhiyuan Ren, Steven Grosz, Iacopo Masi, Xiaoming Liu
• Abstract summary: We present a hierarchical fine-grained formulation for IFDL representation learning. We first represent forgery attributes of a manipulated image with multiple labels at different levels. As a result, the algorithm is encouraged to learn both comprehensive features and inherent hierarchical nature of different forgery attributes.
• Score: 24.595585815686007
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Differences in forgery attributes of images generated in CNN-synthesized and image-editing domains are large, and such differences make a unified image forgery detection and localization (IFDL) challenging. To this end, we present a hierarchical fine-grained formulation for IFDL representation learning. Specifically, we first represent forgery attributes of a manipulated image with multiple labels at different levels. Then we perform fine-grained classification at these levels using the hierarchical dependency between them. As a result, the algorithm is encouraged to learn both comprehensive features and inherent hierarchical nature of different forgery attributes, thereby improving the IFDL representation. Our proposed IFDL framework contains three components: multi-branch feature extractor, localization and classification modules. Each branch of the feature extractor learns to classify forgery attributes at one level, while localization and classification modules segment the pixel-level forgery region and detect image-level forgery, respectively. Lastly, we construct a hierarchical fine-grained dataset to facilitate our study. We demonstrate the effectiveness of our method on $7$ different benchmarks, for both tasks of IFDL and forgery attribute classification. Our source code and dataset can be found: \href{https://github.com/CHELSEA234/HiFi_IFDL}{github.com/CHELSEA234/HiFi-IFDL}.

Related papers

• Language-guided Hierarchical Fine-grained Image Forgery Detection and Localization [17.5445037141816]
  Differences in forgery attributes of images generated in CNN-synthesized and image-editing domains are large. We present a hierarchical fine-grained formulation for IFDL representation learning. As a result, the algorithm is encouraged to learn both comprehensive features and the inherent hierarchical nature of different forgery attributes.
  arXiv  Detail & Related papers  (2024-10-31T01:53:21Z)
• Finetuning CLIP to Reason about Pairwise Differences [52.028073305958074]
  We propose an approach to train vision-language models such as CLIP in a contrastive manner to reason about differences in embedding space. We first demonstrate that our approach yields significantly improved capabilities in ranking images by a certain attribute. We also illustrate that the resulting embeddings obey a larger degree of geometric properties in embedding space.
  arXiv  Detail & Related papers  (2024-09-15T13:02:14Z)
• A Capsule Network for Hierarchical Multi-Label Image Classification [2.507647327384289]
  Hierarchical multi-label classification applies when a multi-class image classification problem is arranged into smaller ones based upon a hierarchy or taxonomy. We propose a multi-label capsule network (ML-CapsNet) for hierarchical classification.
  arXiv  Detail & Related papers  (2022-09-13T04:17:08Z)
• Attribute Group Editing for Reliable Few-shot Image Generation [85.52840521454411]
  We propose a new editing-based method, i.e., Attribute Group Editing (AGE), for few-shot image generation. AGE examines the internal representation learned in GANs and identifies semantically meaningful directions.
  arXiv  Detail & Related papers  (2022-03-16T06:54:09Z)
• DSNet: A Dual-Stream Framework for Weakly-Supervised Gigapixel Pathology Image Analysis [78.78181964748144]
  We present a novel weakly-supervised framework for classifying whole slide images (WSIs) WSIs are commonly processed by patch-wise classification with patch-level labels. With image-level labels only, patch-wise classification would be sub-optimal due to inconsistency between the patch appearance and image-level label.
  arXiv  Detail & Related papers  (2021-09-13T09:10:43Z)
• Semantic Distribution-aware Contrastive Adaptation for Semantic Segmentation [50.621269117524925]
  Domain adaptive semantic segmentation refers to making predictions on a certain target domain with only annotations of a specific source domain. We present a semantic distribution-aware contrastive adaptation algorithm that enables pixel-wise representation alignment. We evaluate SDCA on multiple benchmarks, achieving considerable improvements over existing algorithms.
  arXiv  Detail & Related papers  (2021-05-11T13:21:25Z)
• 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)
• Zero-Shot Recognition through Image-Guided Semantic Classification [9.291055558504588]
  We present a new embedding-based framework for zero-shot learning (ZSL) Motivated by the binary relevance method for multi-label classification, we propose to inversely learn the mapping between an image and a semantic classifier. IGSC is conceptually simple and can be realized by a slight enhancement of an existing deep architecture for classification.
  arXiv  Detail & Related papers  (2020-07-23T06:22:40Z)
• Hierarchical Image Classification using Entailment Cone Embeddings [68.82490011036263]
  We first inject label-hierarchy knowledge into an arbitrary CNN-based classifier. We empirically show that availability of such external semantic information in conjunction with the visual semantics from images boosts overall performance.
  arXiv  Detail & Related papers  (2020-04-02T10:22:02Z)
• Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation [109.89213619785676]
  Few-shot classification aims to recognize novel categories with only few labeled images in each class. Existing metric-based few-shot classification algorithms predict categories by comparing the feature embeddings of query images with those from a few labeled images. While promising performance has been demonstrated, these methods often fail to generalize to unseen domains.
  arXiv  Detail & Related papers  (2020-01-23T18:55:43Z)

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.