论文标题
Syn2real：通过无监督的域适应进行伪造的分类
Syn2Real: Forgery Classification via Unsupervised Domain Adaptation
论文作者
论文摘要
近年来，由于图像处理和计算机视觉技术的现代工具，图像操纵变得越来越容易访问，产生了更自然的图像。识别锻造图像的任务变得非常具有挑战性。在不同类型的伪造中，由于很难检测到这种篡改，因此复制移动伪造的案例正在增加。为了解决此类问题，公开可用的数据集不足。在本文中，我们建议使用深层语义图像介入和复制移动伪造算法创建合成的锻造数据集。但是，在这些数据集上训练的模型在对更现实的数据进行测试时的性能下降。为了减轻此问题，我们使用无监督的域自适应网络通过从合成生成的数据集中映射特征空间来检测新域中的复制移动。此外，我们将CASIA和COMOFOD数据集的F1得分分别提高到80.3％和78.8％。在不可用的数据分类的情况下，我们的方法可能会有所帮助。
In recent years, image manipulation is becoming increasingly more accessible, yielding more natural-looking images, owing to the modern tools in image processing and computer vision techniques. The task of the identification of forged images has become very challenging. Amongst different types of forgeries, the cases of Copy-Move forgery are increasing manifold, due to the difficulties involved to detect this tampering. To tackle such problems, publicly available datasets are insufficient. In this paper, we propose to create a synthetic forged dataset using deep semantic image inpainting and copy-move forgery algorithm. However, models trained on these datasets have a significant drop in performance when tested on more realistic data. To alleviate this problem, we use unsupervised domain adaptation networks to detect copy-move forgery in new domains by mapping the feature space from our synthetically generated dataset. Furthermore, we improvised the F1 score on CASIA and CoMoFoD dataset to 80.3% and 78.8%, respectively. Our approach can be helpful in those cases where the classification of data is unavailable.
