short-paper

Recasting Residual-based Local Descriptors as Convolutional Neural Networks: an Application to Image Forgery Detection

Authors:

Davide Cozzolino,

Giovanni Poggi,

Luisa VerdolivaAuthors Info & Claims

IH&MMSec '17: Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security

Pages 159 - 164

https://doi.org/10.1145/3082031.3083247

Published: 20 June 2017 Publication History

Abstract

Local descriptors based on the image noise residual have proven extremely effective for a number of forensic applications, like forgery detection and localization. Nonetheless, motivated by promising results in computer vision, the focus of the research community is now shifting on deep learning. In this paper we show that a class of residual-based descriptors can be actually regarded as a simple constrained convolutional neural network (CNN). Then, by relaxing the constraints, and fine-tuning the net on a relatively small training set, we obtain a significant performance improvement with respect to the conventional detector.

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Cited By

Cao YShang ZHu ZZhu JLi H(2024)Survey of Intelligent Face Forgery and DetectionJournal of Engineering Studies10.3724/SP.J.1224.2020.0053812:06(538-555)Online publication date: 3-Jan-2024
https://doi.org/10.3724/SP.J.1224.2020.00538
Wei LTianqiang HLiqing HAokun ZChao X(2024)Large-scale datasets for facial tampering detection with inpainting techniquesJournal of Image and Graphics10.11834/jig.23042229:7(1834-1848)Online publication date: 2024
https://doi.org/10.11834/jig.230422
Caibo FChunxiao LYuye WQidang Z(2024)Face forgery detection with image patch comparison and residual map estimationJournal of Image and Graphics10.11834/jig.23014929:2(457-467)Online publication date: 2024
https://doi.org/10.11834/jig.230149
Show More Cited By

Index Terms

Recasting Residual-based Local Descriptors as Convolutional Neural Networks: an Application to Image Forgery Detection
1. Applied computing
  1. Computer forensics
2. Computing methodologies
  1. Machine learning

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Information & Contributors

Information

Published In

cover image ACM Conferences

IH&MMSec '17: Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security

June 2017

180 pages

ISBN:9781450350617

DOI:10.1145/3082031

General Chair:
Matthew C. Stamm
Drexel University, USA
,
Program Chairs:
Matthias Kirchner
SUNY Binghamton, USA
,
Sviatoslav Voloshynovskiy
University of Geneva, Switzerland

Copyright © 2017 ACM.

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SIGMM: ACM Special Interest Group on Multimedia

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New York, NY, United States

Publication History

Published: 20 June 2017

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Short-paper

Funding Sources

Defense Advanced Re search Projects Agency

Conference

IH&MMSec '17

Sponsor:

SIGMM

IH&MMSec '17: ACM Information Hiding and Multimedia Security Workshop

June 20 - 22, 2017

Pennsylvania, Philadelphia, USA

Acceptance Rates

IH&MMSec '17 Paper Acceptance Rate 18 of 34 submissions, 53%;

Overall Acceptance Rate 128 of 318 submissions, 40%

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Cited By

Cao YShang ZHu ZZhu JLi H(2024)Survey of Intelligent Face Forgery and DetectionJournal of Engineering Studies10.3724/SP.J.1224.2020.0053812:06(538-555)Online publication date: 3-Jan-2024
https://doi.org/10.3724/SP.J.1224.2020.00538
Wei LTianqiang HLiqing HAokun ZChao X(2024)Large-scale datasets for facial tampering detection with inpainting techniquesJournal of Image and Graphics10.11834/jig.23042229:7(1834-1848)Online publication date: 2024
https://doi.org/10.11834/jig.230422
Caibo FChunxiao LYuye WQidang Z(2024)Face forgery detection with image patch comparison and residual map estimationJournal of Image and Graphics10.11834/jig.23014929:2(457-467)Online publication date: 2024
https://doi.org/10.11834/jig.230149
Shi ZLiu WChen H(2024)Face Reconstruction-Based Generalized Deepfake Detection Model with Residual Outlook AttentionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3686162Online publication date: 2-Aug-2024
https://doi.org/10.1145/3686162
Zhang JNi JNie FHuang j(2024)Domain-invariant and Patch-discriminative Feature Learning for General Deepfake DetectionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3657297Online publication date: 27-Apr-2024
https://dl.acm.org/doi/10.1145/3657297
Samanta PJain SSerra ESpezzano F(2024)SmartHash: Perceptual Hashing for Image Tampering Detection and AuthenticationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679827(1983-1993)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679827
Weng J(2024)Local frequency analysis for diffusion-generated image detectionInternational Conference on Image Processing and Artificial Intelligence (ICIPAl 2024)10.1117/12.3035198(55)Online publication date: 19-Jul-2024
https://doi.org/10.1117/12.3035198
Smelyakov KKitsenko YChupryna A(2024)Deepfake Detection Models Based on Machine Learning Technologies2024 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream)10.1109/eStream61684.2024.10542582(1-6)Online publication date: 25-Apr-2024
https://doi.org/10.1109/eStream61684.2024.10542582
Lu WLiu LZhang BLuo JZhao XZhou YHuang J(2024)Detection of Deepfake Videos Using Long-Distance AttentionIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.323306335:7(9366-9379)Online publication date: Jul-2024
https://doi.org/10.1109/TNNLS.2022.3233063
Ju YJia SCai JGuan HLyu S(2024)GLFF: Global and Local Feature Fusion for AI-Synthesized Image DetectionIEEE Transactions on Multimedia10.1109/TMM.2023.331350326(4073-4085)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TMM.2023.3313503
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