计算机科学 ›› 2022, Vol. 49 ›› Issue (2): 31-39.doi: 10.11896/jsjkx.210600012
石达, 芦天亮, 杜彦辉, 张建岭, 暴雨轩
SHI Da, LU Tian-liang, DU Yan-hui, ZHANG Jian-ling, BAO Yu-xuan
摘要： 深度伪造可以将人的声音、面部及身体动作拼接,从而合成虚假内容,用于转换性别、改变年龄等。基于生成对抗式图像翻译网络的人脸性别伪造图像存在容易改变无关图像域、人脸细节不够丰富等问题。针对这些问题,文中提出基于改进CycleGAN的人脸性别伪造图像生成模型。首先,优化生成器结构,利用注意力机制与自适应残差块提取更丰富的人脸特征;然后,借鉴相对损失的思想对损失函数进行改进,提高判别器的判别能力。最后,提出基于年龄约束的模型训练策略,减小了年龄变化对生成图像的影响。在CelebA和IMDB-WIKI数据集上进行实验,实验结果表明,与原始CycleGAN方法和UGATIT方法相比,所提方法能够生成更加真实的人脸性别伪造图像,伪造男性和伪造女性的平均内容准确率分别为82.65%和78.83%,FID平均得分分别为32.14和34.50。
中图分类号:
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