3.8 Proceedings Paper

Robust Document Image Forgery Localization Against Image Blending

Related references

Note: Only part of the references are listed.
Article Physics, Multidisciplinary

MFAN: Multi-Level Features Attention Network for Fake Certificate Image Detection

Yu Sun et al.

Summary: This paper investigates forgery detection for certificate images and proposes a novel method called Multi-level Feature Attention Network (MFAN) to tackle this task. The MFAN utilizes a pre-trained residual network and Atrous Spatial Pyramid Pooling (ASPP) to extract features with rich scale information. Experimental results show that the proposed method outperforms other forensics methods.

ENTROPY (2022)

Article Computer Science, Theory & Methods

Robust Image Forgery Detection Against Transmission Over Online Social Networks

Haiwei Wu et al.

Summary: The abuse of image editing software and the prevalence of online social networks have raised concerns about the authenticity of digital images. To combat the spread of forged images on social networks, this research proposes a novel training scheme and provides experimental evidence of its superior performance in detecting image forgeries.

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY (2022)

Article Computer Science, Artificial Intelligence

Document images forgery localization using a two-stream network

Wenbo Xu et al.

Summary: In this paper, a novel two-stream network is proposed for detecting and locating forgery regions in document images. The network captures forgery traces and anomalous features, then integrates features and performs classification with a discriminant network, outperforming state-of-the-art methods in experimental results.

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS (2022)

Article Engineering, Electrical & Electronic

A distortion model-based pre-screening method for document image tampering localization under recapturing attack

Changsheng Chen et al.

Summary: The paper investigates tampering localization methods under recapturing attack. By extracting spectral features and comparing them with reference halftone patterns, a forensic scheme is established. The experimental results show that the proposed method can accurately classify recaptured document images and achieve efficient and accurate tampering localization when combined with CNN models.

SIGNAL PROCESSING (2022)

Proceedings Paper Computer Science, Artificial Intelligence

ObjectFormer for Image Manipulation Detection and Localization

Junke Wang et al.

Summary: This paper proposes an approach called ObjectFormer to detect and localize image manipulations. By extracting high frequency features of the images and combining them with RGB features, subtle manipulation traces that are no longer visible in the RGB domain can be captured. Additionally, a set of learnable object prototypes are used to model object-level consistencies and refine patch embeddings to capture patch-level consistencies.

2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022) (2022)

Proceedings Paper Computer Science, Artificial Intelligence

Image Manipulation Detection by Multi-View Multi-Scale Supervision

Xinru Chen et al.

Summary: The key challenge of image manipulation detection lies in learning features that are sensitive to manipulations in novel data while preventing false alarms on authentic images. This paper addresses both sensitivity and specificity through multi-view feature learning and multi-scale supervision, resulting in a new network called MVSS-Net that demonstrates reliability in detecting pixel-level and image-level manipulations.

2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021) (2021)

Article Computer Science, Theory & Methods

Image Tampering Localization Using a Dense Fully Convolutional Network

Peiyu Zhuang et al.

Summary: The paper introduces a new method for tampering localization in images by identifying commonly used editing tools and operations in Photoshop. A fully convolutional encoder-decoder architecture is designed with dense connections and dilated convolutions to improve localization performance. By generating large-scale training samples using Photoshop scripting, the method demonstrates effectiveness in training the model.

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY (2021)

Proceedings Paper Computer Science, Artificial Intelligence

DOA-GAN: Dual-Order Attentive Generative Adversarial Network for Image Copy-move Forgery Detection and Localization

Ashraful Islam et al.

2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2020)

Proceedings Paper Computer Science, Artificial Intelligence

RRU-Net: The Ringed Residual U-Net for Image Splicing Forgery Detection

Xiuli Bi et al.

2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW 2019) (2019)

Article Computer Science, Information Systems

Image Splicing Localization using a Multi-task Fully Convolutional Network (MFCN)

Ronald Salloum et al.

JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION (2018)

Proceedings Paper Computer Science, Artificial Intelligence

Local binary patterns for document forgery detection

Francisco Cruz et al.

2017 14TH IAPR INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR), VOL 1 (2017)