Joint Learning of Deep Texture and High-Frequency Features for Computer-Generated Image Detection

• URL: http://arxiv.org/abs/2209.03322v1
• Date: Wed, 7 Sep 2022 17:30:40 GMT
• Title: Joint Learning of Deep Texture and High-Frequency Features for Computer-Generated Image Detection
• Authors: Qiang Xu, Shan Jia, Xinghao Jiang, Tanfeng Sun, Zhe Wang, Hong Yan
• Abstract summary: We propose a joint learning strategy with deep texture and high-frequency features for CG image detection. A semantic segmentation map is generated to guide the affine transformation operation. The combination of the original image and the high-frequency components of the original and rendered images are fed into a multi-branch neural network equipped with attention mechanisms.
• Score: 24.098604827919203
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Distinguishing between computer-generated (CG) and natural photographic (PG) images is of great importance to verify the authenticity and originality of digital images. However, the recent cutting-edge generation methods enable high qualities of synthesis in CG images, which makes this challenging task even trickier. To address this issue, a joint learning strategy with deep texture and high-frequency features for CG image detection is proposed. We first formulate and deeply analyze the different acquisition processes of CG and PG images. Based on the finding that multiple different modules in image acquisition will lead to different sensitivity inconsistencies to the convolutional neural network (CNN)-based rendering in images, we propose a deep texture rendering module for texture difference enhancement and discriminative texture representation. Specifically, the semantic segmentation map is generated to guide the affine transformation operation, which is used to recover the texture in different regions of the input image. Then, the combination of the original image and the high-frequency components of the original and rendered images are fed into a multi-branch neural network equipped with attention mechanisms, which refines intermediate features and facilitates trace exploration in spatial and channel dimensions respectively. Extensive experiments on two public datasets and a newly constructed dataset with more realistic and diverse images show that the proposed approach outperforms existing methods in the field by a clear margin. Besides, results also demonstrate the detection robustness and generalization ability of the proposed approach to postprocessing operations and generative adversarial network (GAN) generated images.

Related papers

• Deep Learning Based Speckle Filtering for Polarimetric SAR Images. Application to Sentinel-1 [51.404644401997736]
  We propose a complete framework to remove speckle in polarimetric SAR images using a convolutional neural network. Experiments show that the proposed approach offers exceptional results in both speckle reduction and resolution preservation.
  arXiv  Detail & Related papers  (2024-08-28T10:07:17Z)
• Research on Image Super-Resolution Reconstruction Mechanism based on Convolutional Neural Network [8.739451985459638]
  Super-resolution algorithms transform one or more sets of low-resolution images captured from the same scene into high-resolution images. The extraction of image features and nonlinear mapping methods in the reconstruction process remain challenging for existing algorithms. The objective is to recover high-quality, high-resolution images from low-resolution images.
  arXiv  Detail & Related papers  (2024-07-18T06:50:39Z)
• An efficient dual-branch framework via implicit self-texture enhancement for arbitrary-scale histopathology image super-resolution [18.881480825169053]
  We propose an Implicit Self-Texture Enhancement-based dual-branch framework (ISTE) for arbitrary-scale SR of histopathology images. ISTE outperforms existing fixed-scale and arbitrary-scale SR algorithms across various scaling factors.
  arXiv  Detail & Related papers  (2024-01-28T10:00:45Z)
• Distance Weighted Trans Network for Image Completion [52.318730994423106]
  We propose a new architecture that relies on Distance-based Weighted Transformer (DWT) to better understand the relationships between an image's components. CNNs are used to augment the local texture information of coarse priors. DWT blocks are used to recover certain coarse textures and coherent visual structures.
  arXiv  Detail & Related papers  (2023-10-11T12:46:11Z)
• 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)
• A Robust Approach Towards Distinguishing Natural and Computer Generated Images using Multi-Colorspace fused and Enriched Vision Transformer [0.0]
  This work proposes a robust approach towards distinguishing natural and computer generated images. The proposed approach achieves high performance gain when compared to a set of baselines.
  arXiv  Detail & Related papers  (2023-08-14T17:11:17Z)
• Multi-scale Sparse Representation-Based Shadow Inpainting for Retinal OCT Images [0.261990490798442]
  Inpainting shadowed regions cast by superficial blood vessels in retinal optical coherence tomography ( OCT) images is critical for accurate and robust machine analysis and clinical diagnosis. Traditional sequence-based approaches such as propagating neighboring information to gradually fill in the missing regions are cost-effective. Deep learning-based methods such as encoder-decoder networks have shown promising results in natural image inpainting tasks. We propose a novel multi-scale shadow inpainting framework for OCT images by synergically applying sparse representation and deep learning.
  arXiv  Detail & Related papers  (2022-02-23T09:37:14Z)
• Ensembling with Deep Generative Views [72.70801582346344]
  generative models can synthesize "views" of artificial images that mimic real-world variations, such as changes in color or pose. Here, we investigate whether such views can be applied to real images to benefit downstream analysis tasks such as image classification. We use StyleGAN2 as the source of generative augmentations and investigate this setup on classification tasks involving facial attributes, cat faces, and cars.
  arXiv  Detail & Related papers  (2021-04-29T17:58:35Z)
• Multi-Texture GAN: Exploring the Multi-Scale Texture Translation for Brain MR Images [1.9163481966968943]
  A significant percentage of existing algorithms cannot explicitly exploit and preserve texture details from target scanners. In this paper, we design a multi-scale texture transfer to enrich the reconstruction images with more details. Our method achieves superior results in inter-protocol or inter-scanner translation over state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-02-14T19:14:06Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)
• Learning Enriched Features for Real Image Restoration and Enhancement [166.17296369600774]
  convolutional neural networks (CNNs) have achieved dramatic improvements over conventional approaches for image restoration task. We present a novel architecture with the collective goals of maintaining spatially-precise high-resolution representations through the entire network. Our approach learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details.
  arXiv  Detail & Related papers  (2020-03-15T11:04:30Z)

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.