Wavelet based inpainting detection

• URL: http://arxiv.org/abs/2408.06429v1
• Date: Mon, 12 Aug 2024 18:10:51 GMT
• Title: Wavelet based inpainting detection
• Authors: Barglazan Adrian-Alin, Brad Remus Ovidiu,
• Abstract summary: Inpainting, which is used to remove objects or fill in parts of an image, serves as a powerful tool for both image restoration and forgery. This paper introduces a novel approach for detecting image inpainting forgeries by combining DT-CWT with Hierarchical Feature segmentation and with noise inconsistency analysis. Our approach demonstrates superior results compared with SOTA in detecting inpainted images.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the advancement in image editing tools, manipulating digital images has become alarmingly easy. Inpainting, which is used to remove objects or fill in parts of an image, serves as a powerful tool for both image restoration and forgery. This paper introduces a novel approach for detecting image inpainting forgeries by combining DT-CWT with Hierarchical Feature segmentation and with noise inconsistency analysis. The DT-CWT offers several advantages for this task, including inherent shift-invariance, which makes it robust to minor manipulations during the inpainting process, and directional selectivity, which helps capture subtle artifacts introduced by inpainting in specific frequency bands and orientations. By first applying color image segmentation and then analyzing for each segment, noise inconsistency obtained via DT-CW we can identify patterns indicative of inpainting forgeries. The proposed method is evaluated on a benchmark dataset created for this purpose and is compared with existing forgery detection techniques. Our approach demonstrates superior results compared with SOTA in detecting inpainted images.

Related papers

• Enhanced Wavelet Scattering Network for image inpainting detection [0.0]
  This paper proposes several innovative ideas for detecting inpainting forgeries based on low level noise analysis. It combines Dual-Tree Complex Wavelet Transform (DT-CWT) for feature extraction with convolutional neural networks (CNN) for forged area detection and localization. Our approach was benchmarked against state-of-the-art methods and demonstrated superior performance over all cited alternatives.
  arXiv  Detail & Related papers  (2024-09-25T15:27:05Z)
• Dense Feature Interaction Network for Image Inpainting Localization [28.028361409524457]
  Inpainting can be used to conceal or alter image contents in malicious manipulation of images. Existing methods mostly rely on a basic encoder-decoder structure, which often results in a high number of false positives. In this paper, we describe a new method for inpainting detection based on a Dense Feature Interaction Network (DeFI-Net)
  arXiv  Detail & Related papers  (2024-08-05T02:35:13Z)
• 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)
• CCDWT-GAN: Generative Adversarial Networks Based on Color Channel Using Discrete Wavelet Transform for Document Image Binarization [3.0175628677371935]
  This paper introduces a novelty method employing generative adversarial networks based on color channel. The proposed method involves three stages: image preprocessing, image enhancement, and image binarization. The experimental results demonstrate that CCDWT-GAN achieves a top two performance on multiple benchmark datasets.
  arXiv  Detail & Related papers  (2023-05-27T08:55:56Z)
• DETA: Denoised Task Adaptation for Few-Shot Learning [135.96805271128645]
  Test-time task adaptation in few-shot learning aims to adapt a pre-trained task-agnostic model for capturing taskspecific knowledge. With only a handful of samples available, the adverse effect of either the image noise (a.k.a. X-noise) or the label noise (a.k.a. Y-noise) from support samples can be severely amplified. We propose DEnoised Task Adaptation (DETA), a first, unified image- and label-denoising framework to existing task adaptation approaches.
  arXiv  Detail & Related papers  (2023-03-11T05:23:20Z)
• 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)
• Learning Hierarchical Graph Representation for Image Manipulation Detection [50.04902159383709]
  The objective of image manipulation detection is to identify and locate the manipulated regions in the images. Recent approaches mostly adopt the sophisticated Convolutional Neural Networks (CNNs) to capture the tampering artifacts left in the images. We propose a hierarchical Graph Convolutional Network (HGCN-Net), which consists of two parallel branches.
  arXiv  Detail & Related papers  (2022-01-15T01:54:25Z)
• Inverse Problems Leveraging Pre-trained Contrastive Representations [88.70821497369785]
  We study a new family of inverse problems for recovering representations of corrupted data. We propose a supervised inversion method that uses a contrastive objective to obtain excellent representations for highly corrupted images. Our method outperforms end-to-end baselines even with a fraction of the labeled data in a wide range of forward operators.
  arXiv  Detail & Related papers  (2021-10-14T15:06:30Z)
• Seamless Copy Move Manipulation in Digital Images [0.6445605125467573]
  The proposed method shows good resistance against detection by two frequency domain forgery detection methods from the literature. The purpose of this research work is to create the forgery and highlight the need to produce forgery detection methods that are robust against the malicious copy-move forgery.
  arXiv  Detail & Related papers  (2021-10-12T05:35:26Z)
• 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)
• A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detection [93.38607559281601]
  We devise a hierarchical generative model that captures the multi-scale patch distribution of each training image. The anomaly score is obtained by aggregating the patch-based votes of the correct transformation across scales and image regions.
  arXiv  Detail & Related papers  (2021-04-29T17:49:48Z)

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.