MANNET: A LARGE-SCALE MANIPULATED IMAGE DETECTION DATASET AND BASELINE EVALUATIONS

The sharing of fake content on social media platforms has become a major concern. In many cases, the same content with small variations is shared multiple times on different social media platforms. This leads to the circulation of manipulated content on the web. With the rapid advancement in deep learning algorithms, the generation of manipulated images with small variations in original images has become an easy task. These contents raise serious concerns when used for malicious activities. Therefore, detection of manipulated contents is of paramount importance. However, no large-scale dataset having manipulated images generated using both handcrafted and deep learning algorithms is available. Therefore, in this research, we have proposed a large dataset with more than 5.5 million images, termed as ManNet dataset. Additionally, we have benchmarked the performance of existing algorithms for manipulated image detection. The experimental results highlight that inter-set (disjoint training testing) evaluations are the major challenge of manipulated image detection.