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Encoder-decoder based convolutional neural networks for image forgery detection.
- Source :
- Multimedia Tools & Applications; Jul2022, Vol. 81 Issue 16, p22611-22628, 18p
- Publication Year :
- 2022
-
Abstract
- Today, images editing software has greatly evolved, thanks to them that the semantic manipulation of images has become easier. On the other hand, the identification of these modifications becomes a very difficult task because the modified regions are not visually apparent. In this article, a new convolutional neural network method based on an encoder/decoder called Fals-Unet is proposed to locate the manipulated regions. The encoder of our method uses an architecture topologically identical to that of the Resnet50 method; its main goal is the exploitation of spatial maps to analyze the discriminating characteristics between the manipulated and non-manipulated regions. The decoding network learns the mapping from low-resolution feature maps to pixel-wise predictions for localizing the falsified regions. Finally, the predicted binary mask (0: falsify, 1: not falsify) is generated by the final layer (softmax). Experimental results on many public datasets CASIA, NIST'16, COVERAGE, and COMOD show that the proposed CNN-based model outperforms some methods. [ABSTRACT FROM AUTHOR]
- Subjects :
- CONVOLUTIONAL neural networks
FORGERY
VIDEO coding
EDITING software
Subjects
Details
- Language :
- English
- ISSN :
- 13807501
- Volume :
- 81
- Issue :
- 16
- Database :
- Complementary Index
- Journal :
- Multimedia Tools & Applications
- Publication Type :
- Academic Journal
- Accession number :
- 157587608
- Full Text :
- https://doi.org/10.1007/s11042-020-10158-3