MCDC‐Net: Multi‐scale forgery image detection network based on central difference convolution
| Title: | MCDC‐Net: Multi‐scale forgery image detection network based on central difference convolution |
|---|---|
| Authors: | Defen He, Qian Jiang, Xin Jin, Zien Cheng, Shuai Liu, Shaowen Yao, Wei Zhou |
| Source: | IET Image Processing, Vol 18, Iss 1, Pp 1-12 (2024) |
| Publisher Information: | Wiley, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Computer software |
| Subject Terms: | computer vision, convolutional neural nets, convolution, feature extraction, image processing, image recognition, Photography, TR1-1050, Computer software, QA76.75-76.765 |
| More Details: | Abstract Generative Adversarial Networks (GANs) emerged thanks to the development of deep neural networks. Forgery images generated by various variants of GANs are widely spread on the Internet, which may be damage personal credibility and cause huge property losses. Thus, numerous methods are proposed to detect forgery images, but most of them are designed to detect forgery faces. Therefore, a method to detect forgery images of various scenes is proposed. In this work, central difference convolution and vanilla convolution (CDC‐Mix) are mixed after considering the depth and width features of neural networks and analyzing the influence of attention on network performance. Based on CDC‐Mix, a separable convolution (SeparableCDC‐Mix) is proposed. The proposed method consists of three parts: (1) CDC‐Mix and SeparableCDC‐Mix are used to extract the gradient information and texture features; (2) CDCM is used to extract the multi‐scale information of the image; (3) multi‐scale fusion module (MS‐Fusion) is used to fuse the multi‐scale information from different locations of the network. A large number of experiments have been carried out on several datasets generated by GAN, and the experimental results show that the proposed method has a great improvement compared with the existing advanced methods. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1751-9667 1751-9659 |
| Relation: | https://doaj.org/toc/1751-9659; https://doaj.org/toc/1751-9667 |
| DOI: | 10.1049/ipr2.12928 |
| Access URL: | https://doaj.org/article/364a029bf99343178796ccd1f9c2e0ee |
| Accession Number: | edsdoj.364a029bf99343178796ccd1f9c2e0ee |
| Database: | Directory of Open Access Journals |
| ISSN: | 17519667 17519659 |
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| DOI: | 10.1049/ipr2.12928 |
| Published in: | IET Image Processing |
| Language: | English |