Noise-attention-based forgery face detection method
With the advancement of artificial intelligence and deep neural networks, the ease of image generation and editing has increased significantly.Consequently, the occurrence of malicious tampering and forgery using image generation tools is on the rise, posing a significant threat to multimedia securi...
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| Format: | Article |
| Language: | English |
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POSTS&TELECOM PRESS Co., LTD
2023-08-01
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| Series: | 网络与信息安全学报 |
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| Online Access: | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2023061 |
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| author | Bolin ZHANG Chuntao ZHU Qilin YIN Jingqiao FU Lingyi LIU Jiarui LIU Hongmei LIU Wei LU |
| author_facet | Bolin ZHANG Chuntao ZHU Qilin YIN Jingqiao FU Lingyi LIU Jiarui LIU Hongmei LIU Wei LU |
| author_sort | Bolin ZHANG |
| collection | DOAJ |
| description | With the advancement of artificial intelligence and deep neural networks, the ease of image generation and editing has increased significantly.Consequently, the occurrence of malicious tampering and forgery using image generation tools is on the rise, posing a significant threat to multimedia security and social stability.Therefore, it is crucial to research detection methods for forged faces.Face tampering and forgery can occur through various means and tools, leaving different levels of forgery traces during the tampering process.These traces can be partly reflected in the image noise.From the perspective of image noise, the noise components reflecting tampering traces of forged faces were extracted through a noise removal module.Furthermore, noise attention was generated to guide the backbone network in the detection of forged faces.The training of the noise removal module was supervised using SRM filters.In order to strengthen the guidance of the noise removal module, the noise obtained by the noise removal module was added back to the real face image, forming a pair of supervised training samples in a self-supervised manner.The experimental results illustrate that the noise features obtained by the noise removal module have a good degree of discrimination.Experiments were also conducted on several public datasets, and the proposed method achieves an accuracy of 98.32% on the Celeb-DF dataset, 92.61% on the DFDC dataset, and more than 94% on the FaceForensics++ dataset, thus proving the effectiveness of the proposed method. |
| format | Article |
| id | doaj-art-fdb4dd9f322c48bf9802e3a8c85d3a85 |
| institution | Kabale University |
| issn | 2096-109X |
| language | English |
| publishDate | 2023-08-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 网络与信息安全学报 |
| spelling | doaj-art-fdb4dd9f322c48bf9802e3a8c85d3a852025-01-15T03:16:48ZengPOSTS&TELECOM PRESS Co., LTD网络与信息安全学报2096-109X2023-08-01915516559579930Noise-attention-based forgery face detection methodBolin ZHANGChuntao ZHUQilin YINJingqiao FULingyi LIUJiarui LIUHongmei LIUWei LUWith the advancement of artificial intelligence and deep neural networks, the ease of image generation and editing has increased significantly.Consequently, the occurrence of malicious tampering and forgery using image generation tools is on the rise, posing a significant threat to multimedia security and social stability.Therefore, it is crucial to research detection methods for forged faces.Face tampering and forgery can occur through various means and tools, leaving different levels of forgery traces during the tampering process.These traces can be partly reflected in the image noise.From the perspective of image noise, the noise components reflecting tampering traces of forged faces were extracted through a noise removal module.Furthermore, noise attention was generated to guide the backbone network in the detection of forged faces.The training of the noise removal module was supervised using SRM filters.In order to strengthen the guidance of the noise removal module, the noise obtained by the noise removal module was added back to the real face image, forming a pair of supervised training samples in a self-supervised manner.The experimental results illustrate that the noise features obtained by the noise removal module have a good degree of discrimination.Experiments were also conducted on several public datasets, and the proposed method achieves an accuracy of 98.32% on the Celeb-DF dataset, 92.61% on the DFDC dataset, and more than 94% on the FaceForensics++ dataset, thus proving the effectiveness of the proposed method.http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2023061Deepfake detectionimage noiseattention mechanismtempering artifact |
| spellingShingle | Bolin ZHANG Chuntao ZHU Qilin YIN Jingqiao FU Lingyi LIU Jiarui LIU Hongmei LIU Wei LU Noise-attention-based forgery face detection method 网络与信息安全学报 Deepfake detection image noise attention mechanism tempering artifact |
| title | Noise-attention-based forgery face detection method |
| title_full | Noise-attention-based forgery face detection method |
| title_fullStr | Noise-attention-based forgery face detection method |
| title_full_unstemmed | Noise-attention-based forgery face detection method |
| title_short | Noise-attention-based forgery face detection method |
| title_sort | noise attention based forgery face detection method |
| topic | Deepfake detection image noise attention mechanism tempering artifact |
| url | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2023061 |
| work_keys_str_mv | AT bolinzhang noiseattentionbasedforgeryfacedetectionmethod AT chuntaozhu noiseattentionbasedforgeryfacedetectionmethod AT qilinyin noiseattentionbasedforgeryfacedetectionmethod AT jingqiaofu noiseattentionbasedforgeryfacedetectionmethod AT lingyiliu noiseattentionbasedforgeryfacedetectionmethod AT jiaruiliu noiseattentionbasedforgeryfacedetectionmethod AT hongmeiliu noiseattentionbasedforgeryfacedetectionmethod AT weilu noiseattentionbasedforgeryfacedetectionmethod |