DOA-GAN: Dual-Order Attentive GAN for Image Copy-move Forgery Detection and LocalizationAshraful Islam, Chengjiang Long*, Arslan Basharat, Anthony Hoogs

Rensselaer Polytechnic Institute, Kitware Inc.islama6@rpi.edu, *cjfykx@gmail.com, {arsaln.Basharat, Anthony.hoogs}@kitware.com

Problem & Background• Copy-move image forgery could be used to add or hide

some objects appearing a digital image, leading to a different interpretation.

• It could be misled if such a manipulated image was part of a criminal investigation à It is crucial to develop a robust image forensic tool for copy-move detection and localization.

• The results of existing approaches are still far from perfect. It is very challenging to distinguish copy-moves from incidental similarities, which occur frequently.

Proposed Approach Robustness Analysis

Contributions• We propose a dual-order attentive Generative Adversarial

Network for image copy-move forgery detection and localization.

• Our 1st-order attention module is able to extract the copy-move location aware attention map and the 2nd-order attention module explores pixel-to-pixel inter-dependence.

• Extensive experiments strongly demonstrate that the proposed DOA-GAN clearly outperforms state-of-the-art.

1st/2nd-order Attention Module

Datasets and MetricsDatasets: USC-ISI, CASIA, and CoMoFD; Metrics: recall, precision,F1 for both pixel-wise localization and image-level detection.

Key References[BursterNet] Y. Wu, et al. BusterNet: Detecting copy-move image forgery with source/target localization. ECCV, 2018.[AdaptiveSeg] C. Pun et al. Image forgery detection using adaptive over-segmentationand feature point matching. TIFS, 2015.[DenseField] D. Cozzolino et al. Efficient dense-field copy–move forgery detection. TIFS, 2015.

[PatchMatch] L. Amiano, et al. A patch match-based dense-field lgorithm for video copy–move detection and localization. TCSVT, 2018.[DMVN] Y. Wu et al. Deep matching and validation network: An end-toend solution to constrained image splicing localization and detection. ACM MM, 2017[DMAC] Y. Liu et al. Adversarial learning for image forensics deepmatching with atrous convolution. arXiv, 2018

CASIA CoMoFoD This research was developed with funding from the Defense Advanced Research Projects Agency (DARPA) under Contract No.FA875016-C-0166. The views, opinions and/or findings expressed are those of the authors.

Acknowledgement

From left to right are original,forged, and GT images. Ourgoal is to automatically detectand localize the source(green) and the target (red)regions in forged images.

Quantitative Results Qualitative Results

From left to right are the input image; results of Adaptive-Seg DenseField, BusterNet , and our DOA-GAN; and the GT mask. (top: CASIA; bottom: CoMoFoD)

F1 scores on CoMoFoD under attacks.

Number of correctly detected images on CoMoFoD under attacks.

Invariance Analysis on our self-collected dataset generated from MS-COCO.

Failure CasesFailure cases: (1) whenthe copy region is just extracted from the uniform background and pasted on the same background, (2)when the scale has been changed significantly.

Extension to Other Manipulation Types

Image splicing on MS-COCO

Video copy-move on self-collecteddataset.

Input images, results of DMVN, DMAC, DOA-GAN, and GT mask.

DOA-GAN

GT mask

USC-ISI

∗This work was closely supervised by Chengjiang Long when Ashraful Islam was a summer intern at Kitware, Inc.