IS&T | Library

Detection, Attribution and Localization of GAN Generated Images

170 9

Image forensics
GAN image detection
Multimedia security
Deep learning
Fake images

Michael Goebel, Lakshmanan Nataraj, Tejaswi Nanjundaswamy, Tajuddin Manhar Mohammed, Shivkumar Chandrasekaran, B. S. Manjunath

Pages 276-1 - 276-11, January 2021, © Society for Imaging Science and Technology 2021

DOI

10.2352/ISSN.2470-1173.2021.4.MWSF-276

Volume 33

Issue 4

Recent advances in Generative Adversarial Networks (GANs) have led to the creation of realistic-looking digital images that pose a major challenge to their detection by humans or computers. GANs are used in a wide range of tasks, from modifying small attributes of an image (StarGAN [14]), transferring attributes between image pairs (CycleGAN [92]), as well as generating entirely new images (ProGAN [37], StyleGAN [38], SPADE/GauGAN [65]). In this paper, we propose a novel approach to detect, attribute and localize GAN generated images that combines image features with deep learning methods. For every image, co-occurrence matrices are computed on neighborhood pixels of RGB channels in different directions (horizontal, vertical and diagonal). A deep learning network is then trained on these features to detect, attribute and localize these GAN generated/manipulated images. A large scale evaluation of our approach on 5 GAN datasets comprising over 2.76 million images (ProGAN, StarGAN, CycleGAN, StyleGAN and SPADE/GauGAN) shows promising results in detecting GAN generated images.

Digital Library: EI

Published Online: January 2021

Holistic Image Manipulation Detection using Pixel Cooccurrence Matrices

63 16

Image forensics
Holistic image manipulation detection
Multimedia security
Deep learning
Fake images

Lakshmanan Nataraj, Michael Goebel, Tajuddin Manhar Mohammed, Shivkumar Chandrasekaran, B. S. Manjunath

Pages 277-1 - 277-7, January 2021, © Society for Imaging Science and Technology 2021

DOI

10.2352/ISSN.2470-1173.2021.4.MWSF-277

Volume 33

Issue 4

Digital image forensics aims to detect images that have been digitally manipulated. Realistic image forgeries involve a combination of splicing, resampling, region removal, smoothing and other manipulation methods. While most detection methods in literature focus on detecting a particular type of manipulation, it is challenging to identify doctored images that involve a host of manipulations. In this paper, we propose a novel approach to holistically detect tampered images using a combination of pixel co-occurrence matrices and deep learning. We extract horizontal and vertical co-occurrence matrices on three color channels in the pixel domain and train a model using a deep convolutional neural network (CNN) framework. Our method is agnostic to the type of manipulation and classifies an image as tampered or untampered. We train and validate our model on a dataset of more than 86,000 images. Experimental results show that our approach is promising and achieves more than 0.99 area under the curve (AUC) evaluation metric on the training and validation subsets. Further, our approach also generalizes well and achieves around 0.81 AUC on an unseen test dataset comprising more than 19,740 images released as part of the Media Forensics Challenge (MFC) 2020. Our score was highest among all other teams that participated in the challenge, at the time of announcement of the challenge results.

Digital Library: EI

Published Online: January 2021

Detecting GAN generated Fake Images using Co-occurrence Matrices

485 103

Image forensics
GAN image detection
Fake news
Multimedia security
Deep Learning
Fake images

Lakshmanan Nataraj, Tajuddin Manhar Mohammed, B. S. Manjunath, Shivkumar Chandrasekaran, Arjuna Flenner, Jawadul H. Bappy, Amit K. Roy-Chowdhury

Pages 532-1 - 532-7, January 2019, © Society for Imaging Science and Technology 2019

DOI

10.2352/ISSN.2470-1173.2019.5.MWSF-532

Volume 31

Issue 5

The advent of Generative Adversarial Networks (GANs) has brought about completely novel ways of transforming and manipulating pixels in digital images. GAN based techniques such as Image-to-Image translations, DeepFakes, and other automated methods have become increasingly popular in creating fake images. In this paper, we propose a novel approach to detect GAN generated fake images using a combination of co-occurrence matrices and deep learning. We extract co-occurrence matrices on three color channels in the pixel domain and train a model using a deep convolutional neural network (CNN) framework. Experimental results on two diverse and challenging GAN datasets comprising more than 56,000 images based on unpaired image-to-image translations (cycleGAN [1]) and facial attributes/expressions (StarGAN [2]) show that our approach is promising and achieves more than 99% classification accuracy in both datasets. Further, our approach also generalizes well and achieves good results when trained on one dataset and tested on the other.

Digital Library: EI

Published Online: January 2019

Boosting Image Forgery Detection using Resampling Features and Copy-move Analysis

73 2

Image forensics
Multimedia security
Feature extraction
Radon transform
Deep learning
Image segmentation
Statistical analysis
Copy-move forgery

Tajuddin Manhar Mohammed, Jason Bunk, Lakshmanan Nataraj, Jawadul H. Bappy, Arjuna Flenner, B.S. Manjunath, Shivkumar Chandrasekaran, Amit K. Roy-Chowdhury, Lawrence A. Peterson

DOI

10.2352/ISSN.2470-1173.2018.07.MWSF-118

Volume 30

Issue 7

Realistic image forgeries involve a combination of splicing, resampling, cloning, region removal and other methods. While resampling detection algorithms are effective in detecting splicing and resampling, copy-move detection algorithms excel in detecting cloning and region removal. In this paper, we combine these complementary approaches in a way that boosts the overall accuracy of image manipulation detection. We use the copy-move detection method as a pre-filtering step and pass those images that are classified as untampered to a deep learning based resampling detection framework. Experimental results on various datasets including the 2017 NIST Nimble Challenge Evaluation dataset comprising nearly 10,000 pristine and tampered images shows that there is a consistent increase of 8%-10% in detection rates, when copy-move algorithm is combined with different resampling detection algorithms.

Digital Library: EI

Published Online: January 2018

Resampling Forgery Detection Using Deep Learning and A-Contrario Analysis

66 4

Image forensics
Multimedia security
Deep learning
Image segmentation
Statistical analysis

A. Flenner, L. Peterson, J. Bunk, T.M. Mohammed, L. Nataraj, B.S. Manjunath

DOI

10.2352/ISSN.2470-1173.2018.07.MWSF-212

Volume 30

Issue 7