Two-Stream Neural Networks for Tampered Face Detection

We propose a two-stream network for face tampering detection. We train GoogLeNet to detect tampering artifacts in a face classification stream, and train a patch based triplet network to leverage features capturing local noise residuals and camera characteristics as a second stream. In addition, we use two different online face swaping applications to create a new dataset that consists of 2010 tampered images, each of which contains a tampered face. We evaluate the proposed two-stream network on our newly collected dataset. Experimental results demonstrate the effectness of our method.

• Publication year

  2017

• Venue

  2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

• Publication date

  2017-07-21

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1109/CVPRW.2017.229arXiv 1803.11276
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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