No way to crop: On robust image crop localization

• URL: http://arxiv.org/abs/2110.05687v1
• Date: Tue, 12 Oct 2021 02:19:42 GMT
• Title: No way to crop: On robust image crop localization
• Authors: Qichao Ying, Xiaoxiao Hu, Hang Zhou, Xiangyu Zhang, Zhengxin You and Zhenxing Qian
• Abstract summary: This paper presents a novel scheme for image crop localization using robust watermarking. We further extend our scheme to detect tampering attack on the attacked image.
• Score: 18.714976804821738
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Previous image forensics schemes for crop detection are only limited on predicting whether an image has been cropped. This paper presents a novel scheme for image crop localization using robust watermarking. We further extend our scheme to detect tampering attack on the attacked image. We demonstrate that our scheme is the first to provide high-accuracy and robust image crop localization. Besides, the accuracy of tamper detection is comparable to many state-of-the-art methods.

Related papers

• SWIFT: Semantic Watermarking for Image Forgery Thwarting [12.515429388063534]
  We modify the HiDDeN deep-learning watermarking architecture to embed and extract high-dimensional real vectors representing image captions. Our method improves significantly on both malign and benign edits.
  arXiv  Detail & Related papers  (2024-07-26T09:50:13Z)
• Certifiably Robust Image Watermark [57.546016845801134]
  Generative AI raises many societal concerns such as boosting disinformation and propaganda campaigns. Watermarking AI-generated content is a key technology to address these concerns. We propose the first image watermarks with certified robustness guarantees against removal and forgery attacks.
  arXiv  Detail & Related papers  (2024-07-04T17:56:04Z)
• Robustness of AI-Image Detectors: Fundamental Limits and Practical Attacks [47.04650443491879]
  We analyze the robustness of various AI-image detectors including watermarking and deepfake detectors. We show that watermarking methods are vulnerable to spoofing attacks where the attacker aims to have real images identified as watermarked ones.
  arXiv  Detail & Related papers  (2023-09-29T18:30:29Z)
• Robust Image Protection Countering Cropping Manipulation [30.185576617722713]
  This paper presents a novel robust watermarking scheme for image Cropping localization and Recovery (CLR-Net) We first protect the original image by introducing imperceptible perturbations. Then, typical image post-processing attacks are simulated to erode the protected image. On the recipient's side, we predict the cropping mask and recover the original image.
  arXiv  Detail & Related papers  (2022-06-06T07:26:29Z)
• Detecting and Localizing Copy-Move and Image-Splicing Forgery [0.0]
  We focus on the methods to detect if an image has been tampered with using both Deep Learning and Image transformation methods. We then attempt to identify the tampered area of the image and predict the corresponding mask. Based on the results, suggestions and approaches are provided to achieve a more robust framework to detect and identify the forgeries.
  arXiv  Detail & Related papers  (2022-02-08T01:14:30Z)
• Watermarking Images in Self-Supervised Latent Spaces [75.99287942537138]
  We revisit watermarking techniques based on pre-trained deep networks, in the light of self-supervised approaches. We present a way to embed both marks and binary messages into their latent spaces, leveraging data augmentation at marking time.
  arXiv  Detail & Related papers  (2021-12-17T15:52:46Z)
• SeeTheSeams: Localized Detection of Seam Carving based Image Forgery in Satellite Imagery [15.127101376238418]
  Seam carving is a popular technique for content aware image manipulation. This paper proposes a novel approach for detecting and localizing seams in such images.
  arXiv  Detail & Related papers  (2021-08-28T00:00:37Z)
• 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)
• Instance Localization for Self-supervised Detection Pretraining [68.24102560821623]
  We propose a new self-supervised pretext task, called instance localization. We show that integration of bounding boxes into pretraining promotes better task alignment and architecture alignment for transfer learning. Experimental results demonstrate that our approach yields state-of-the-art transfer learning results for object detection.
  arXiv  Detail & Related papers  (2021-02-16T17:58:57Z)
• Deep Photo Cropper and Enhancer [65.11910918427296]
  We propose a new type of image enhancement problem: to crop an image which is embedded within a photo. We split our proposed approach into two deep networks: deep photo cropper and deep image enhancer. In the photo cropper network, we employ a spatial transformer to extract the embedded image. In the photo enhancer, we employ super-resolution to increase the number of pixels in the embedded image.
  arXiv  Detail & Related papers  (2020-08-03T03:50:20Z)
• Robust watermarking with double detector-discriminator approach [0.5330240017302621]
  We present a novel deep framework for a watermarking - a technique of embedding a transparent message into an image in a way that allows retrieving the message from a copy. Our framework outperforms recent methods in the context of robustness against spectrum of attacks. We also present our double detector-discriminator approach - a scheme to detect and discriminate if the image contains the embedded message or not.
  arXiv  Detail & Related papers  (2020-06-06T17:15:45Z)

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.