摘要
基于深度学习的图像伪造方法生成的图像肉眼难辨,一旦该技术被滥用于制作虚假图像和视频,可能会对国家政治、经济、文化造成严重的负面影响,也可能会对社会生活和个人隐私构成威胁。针对上述问题,提出了一种基于自动编码器的深度伪造Deepfake图像检测方法。首先,借助高斯滤波对图像进行预处理,提取高频信息作为模型输入;然后,利用自动编码器对图像进行特征提取,并在编码器中添加注意力机制模块以获取更好的分类效果;最后,通过消融实验证明,采用所提的预处理方法和添加注意力机制模块有助于伪造图像检测。实验结果表明,与ResNet50、Xception以及InceptionV3相比,所提方法在数据集样本量较小且包含的场景丰富时,可以有效检测多种生成方法所伪造的图像,其平均准确率可达97.10%,明显优于对比方法,且其泛化性能也明显优于对比方法。
The image forgery method based on deep learning can generate images which are difficult to distinguish with the human eye.Once the technology is abused to produce fake images and videos,it will have a serious negative impact on a country’s politics,economy,and culture,as well as the social life and personal privacy.To solve the problem,a Deepfake detection method based on autoencoder was proposed.Firstly,the Gaussian filtering was used to preprocess the image,and the high-frequency information was extracted as the input of the model.Secondly,the autoencoder was used to extract features from the image.In order to obtain better classification effect,an attention mechanism module was added to the encoder.Finally,it was proved by the ablation experiments that the proposed preprocessing method and the addition of attention mechanism module were helpful for the Deepfake image detection.Experimental results show that,compared with ResNet50,Xception and InceptionV3,the proposed method can effectively detect images forged by multiple generation methods when the dataset has a small sample size and contains multiple scenes,and its average accuracy is up to 97.10%,which is significantly better than those of the comparison methods,and its generalization performance is also significantly better than those of the comparison methods.
作者
张亚
金鑫
江倩
李昕洁
董云云
姚绍文
ZHANG Ya;JIN Xin;JIANG Qian;LEE Shin-jye;DONG Yunyun;YAO Shaowen(School of Software,Yunnan University,Kunming Yunnan 650504,China;Cross-border Cyberspace Security Engineering Research Center of Ministry of Education(Yunnan University),Kunming Yunnan 650504,China;Institute of Technology Management,Yang Ming Chiao Tung University,Hsinchu Taiwan 300093,China)
出处
《计算机应用》
CSCD
北大核心
2021年第10期2985-2990,共6页
journal of Computer Applications
基金
国家自然科学基金资助项目(62002313,61863036)
中国博士后科学基金资助项目(2020T130564,2019M653507)
云南省重点研发领域计划项目(202001BB050076)
云南省软件工程重点实验室开放基金资助项目(2020SE408)
云南省博士后科研基金资助项目
云南大学第12届研究生科研创新项目(2020230,2020231)
