Adversarial Learning for Constrained Image Splicing Detection and Localization Based on Atrous Convolution

You are here

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

Adversarial Learning for Constrained Image Splicing Detection and Localization Based on Atrous Convolution

Yaqi Liu; Xiaobin Zhu; Xianfeng Zhao; Yun Cao

Constrained image splicing detection and localization (CISDL), which investigates two input suspected images and identifies whether one image has suspected regions pasted from the other, is a newly proposed challenging task for image forensics. In this paper, we propose a novel adversarial learning framework to learn a deep matching network for CISDL. Our framework mainly consists of three building blocks. First, a deep matching network based on atrous convolution (DMAC) aims to generate two high-quality candidate masks, which indicate suspected regions of the two input images. In DMAC, atrous convolution is adopted to extract features with rich spatial information, a correlation layer based on a skip architecture is proposed to capture hierarchical features, and atrous spatial pyramid pooling is constructed to localize tampered regions at multiple scales. Second, a detection network is designed to rectify inconsistencies between the two corresponding candidate masks. Finally, a discriminative network drives the DMAC network to produce masks that are hard to distinguish from ground-truth ones. The detection network and the discriminative network collaboratively supervise the training of DMAC in an adversarial way. Besides, a sliding window-based matching strategy is investigated for high-resolution images matching. Extensive experiments, conducted on five groups of datasets, demonstrate the effectiveness of the proposed framework and the superior performance of DMAC.

SPS on Twitter

  • Join us on Friday, 21 May at 1:00 PM EST when Dr. Amir Asif (York University) shares his journey and the importance…
  • There's still time to apply for PROGRESS! Visit to connect with signal processing leaders a…
  • This Saturday, 8 May, join the SPS JSS Academy of Technical Education Noida Student Branch Chapter in collaboration…
  • The SPACE Webinar Series continues this Tuesday, 4 May at 10:00 AM Eastern when Dr. Lei Tian presents "Modeling and…
  • The second annual IEEE SIGHT Day will take place on 28 April! This year’s theme is “Celebrating 10 years of IEEE SI…

SPS Videos

Signal Processing in Home Assistants


Multimedia Forensics

Careers in Signal Processing             


Under the Radar