Reality transform adversarial generators for image splicing forgery detection and localization
X Bi, Z Zhang, B Xiao - proceedings of the IEEE/CVF …, 2021 - openaccess.thecvf.com
When many forged images become more and more realistic with the help of image editing
tools and deep learning techniques, authenticators need to improve their ability to verify …
tools and deep learning techniques, authenticators need to improve their ability to verify …
TBNet: A two-stream boundary-aware network for generic image manipulation localization
Finding tampered regions in images is a common research topic in machine learning and
computer vision. Although many image manipulation location algorithms have been …
computer vision. Although many image manipulation location algorithms have been …
A dense u-net with cross-layer intersection for detection and localization of image forgery
R Zhang, J Ni - … 2020-2020 IEEE international conference on …, 2020 - ieeexplore.ieee.org
In this paper, we apply cross-layer intersection mechanism to dense u-net for image forgery
detection and localization. We first train DenseNet for binary classification. Spatial rich …
detection and localization. We first train DenseNet for binary classification. Spatial rich …
Content-aware detection of JPEG grid inconsistencies for intuitive image forensics
The paper proposes a novel method for detecting indicators of image forgery by locating grid
alignment abnormalities in JPEG compressed image bitmaps. The method evaluates …
alignment abnormalities in JPEG compressed image bitmaps. The method evaluates …
Hybrid features and semantic reinforcement network for image forgery detection
H Chen, C Chang, Z Shi, Y Lyu - Multimedia Systems, 2022 - Springer
Image forgery detection focuses more on tampering regions than image content of semantic
segmentation, it is revealed that wealthier features need to be learned. Moreover, insufficient …
segmentation, it is revealed that wealthier features need to be learned. Moreover, insufficient …
A deep learning approach for iris sensor model identification
The aim of this paper is to propose an algorithm based on convolutional neural networks
(CNN) for iris sensor model identification. This task is important in forensics applications as …
(CNN) for iris sensor model identification. This task is important in forensics applications as …
Image manipulation localization using multi-scale feature fusion and adaptive edge supervision
Image manipulation localization is a technique that can efficiently segment the tampered
regions from a suspicious image. Existing work usually trains a detection model by fusing …
regions from a suspicious image. Existing work usually trains a detection model by fusing …
TBFormer: Two-branch transformer for image forgery localization
Y Liu, B Lv, X Jin, X Chen… - IEEE Signal Processing …, 2023 - ieeexplore.ieee.org
Image forgery localization aims to identify forged regions by capturing subtle traces from
high-quality discriminative features. In this paper, we propose a Transformer-style network …
high-quality discriminative features. In this paper, we propose a Transformer-style network …
MFI-Net: Multi-Feature Fusion Identification Networks for Artificial Intelligence Manipulation
R Ren, Q Hao, S Niu, K Xiong, J Zhang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Tampered images can easily be used for illegal activities, such as spreading rumors,
economic fraud, fabricating false news, and illegally obtaining experience benefits, etc. With …
economic fraud, fabricating false news, and illegally obtaining experience benefits, etc. With …
Comprint: Image forgery detection and localization using compression fingerprints
H Mareen, D Vanden Bussche, F Guillaro… - … Conference on Pattern …, 2022 - Springer
Manipulation tools that realistically edit images are widely available, making it easy for
anyone to create and spread misinformation. In an attempt to fight fake news, forgery …
anyone to create and spread misinformation. In an attempt to fight fake news, forgery …