FBI-Net: Frequency-Based Image Forgery Localization via Multitask Learning With Self-Attention
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3182024
Abstract: Image forgery is easily manufactured for illegal acts such as spreading misleading information, which can have unfortunate consequences for society. In this work, we propose a Discrete Cosine Transformation (DCT) based multi-task learning network named… read more here.
Keywords: fbi net; localization; frequency; attention ... See more keywords
Learning Traces by Yourself: Blind Image Forgery Localization via Anomaly Detection With ViT-VAE
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2023.3245947
Abstract: Most of existing deep learning models for image forgery localization rely on a large number of high-quality labeled samples for training. The training procedures are performed off-line and without adaptation to the image under scrutiny.… read more here.
Keywords: image forgery; forgery localization; vae; image ... See more keywords
TBFormer: Two-Branch Transformer for Image Forgery Localization
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2023.3279018
Abstract: 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 with two feature extraction branches for image forgery localization, and it… read more here.
Keywords: image forgery; two branch; forgery localization; feature ... See more keywords
DiRLoc: Disentanglement Representation Learning for Robust Image Forgery Localization
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Dependable and Secure Computing"
DOI: 10.1109/tdsc.2024.3522190
Abstract: Deep Learning image forgery localization methods have achieved remarkable results but cannot maintain comparable performance when the forgery images are JPEG compressed, a format that is widely used in daily information transmission. The robustness against… read more here.
Keywords: image; disentanglement; forgery localization; forgery ... See more keywords
FLDCF: A Collaborative Framework for Forgery Localization and Detection in Satellite Imagery
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DOI: 10.1109/tgrs.2024.3502035
Abstract: Satellite images are highly susceptible to forgery due to various editing techniques. Traditional forgery detection methods, designed for natural images, often fail when applied to satellite images because of differences in sensing technology and processing… read more here.
Keywords: detection; forgery localization; collaborative framework; localization detection ... See more keywords
Employing Reinforcement Learning to Construct a Decision-Making Environment for Image Forgery Localization
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Information Forensics and Security"
DOI: 10.1109/tifs.2024.3381470
Abstract: The widespread misuse of advanced image editing tools and deep generative techniques has led to a proliferation of images with altered content in real-life scenarios, often without any discernible traces of tampering. This has created… read more here.
Keywords: image; forgery localization; decision; image forgery ... See more keywords
A Forensic Framework With Diverse Data Generation for Generalizable Forgery Localization
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Information Forensics and Security"
DOI: 10.1109/tifs.2025.3607251
Abstract: Deep learning-based forensic techniques have emerged as the leading approach for image forgery localization. However, many existing methods struggle with overfitting to the training data, which limits their generalization performance and real-world applicability. To overcome… read more here.
Keywords: forensic framework; framework; forgery localization; data generation ... See more keywords
Toward Adaptive Unsupervised and Blind Image Forgery Localization with ViT-VAE and a Gaussian Mixture Model
Sign Up to like & getrecommendations! Published in 2025 at "Mathematics"
DOI: 10.3390/math13142285
Abstract: Most image forgery localization methods rely on supervised learning, requiring large labeled datasets for training. Recently, several unsupervised approaches based on the variational autoencoder (VAE) framework have been proposed for forged pixel detection. In these… read more here.
Keywords: forgery localization; image forgery; gaussian mixture;