Image forgery is a topic that has been studied for many years. Before the breakthrough of deep learning, forged images were detected using handcrafted features that did not require training. These traditional methods failed to perform satisfactorily even on datasets much worse in quality than real-life image manipulations. Advances in deep learning have impacted image forgery detection as much as they have impacted other areas of computer vision and have improved the state of the art. Deep learning models require large amounts of labeled data for training. In the case of image forgery, labeled data at the pixel level is a very important factor for the models to learn. None of the existing datasets have sufficient size, realism and pixel-level labeling at the same time. This is due to the high cost of producing and labeling quality images. It can take hours for an image editing expert to manipulate just one image. To bridge this gap, we automate data generation using image composition techniques that are very related to image forgery. Unlike other automated data generation frameworks, we use state of the art image composition deep learning models to generate spliced images close to the quality of real-life manipulations. Finally, we test the generated dataset on the SOTA image manipulation detection model and show that its prediction performance is lower compared to existing datasets, i.e. we produce realistic images that are more difficult to detect. Dataset will be available at https://github.com/99eren99/DIS25k .

翻译：影像伪造是一个已研究多年的课题。在深度学习取得突破之前，伪造影像检测主要依赖无需训练的手工特征。这些传统方法即使在质量远逊于真实影像篡改的数据集上，也难以达到令人满意的性能。深度学习的发展对影像伪造检测的影响与其对其他计算机视觉领域的影响一样深远，并推动了该领域技术水平的提升。深度学习模型需要大量标注数据进行训练，而在影像伪造中，像素级标注数据对模型学习至关重要。然而，现有数据集均无法同时满足规模、真实性和像素级标注的要求，这是因为高质量影像的生成与标注成本高昂——影像编辑专家篡改单张图像可能需要数小时。为弥补这一不足，我们利用与影像伪造高度相关的影像合成技术，实现数据生成的自动化。与其他自动化数据生成框架不同，我们采用最先进的影像合成深度学习模型，生成接近真实篡改质量的拼接影像。最终，我们使用当前最优的影像篡改检测模型对生成的数据集进行测试，结果表明其预测性能低于现有数据集——即我们生成了更难以检测的高保真影像。该数据集将在 https://github.com/99eren99/DIS25k 公布。