In recent times, digital image forensics is gaining increased attention in multimedia forensics owing to the widespread scam alertness. Several forensic methods have been studied to establish the integrity of digital images by disclosing manipulation fingerprints. Anti-forensic (AF) attacks on manipulated images, particularly deep learning-based adversarial attacks using generative adversarial network (GAN), have been successfully applied to delude forensic methods. Consequently, an efficacious, efficient, and robust counter-AF (CAF) method is required to secure the integrity of digital images. In this study, we propose a robust open-set multi-instance learning approach for exposing GAN-based AF on manipulated images by introducing additional GAN-based operations. First, we generate multiple real instances from real images using multiple additional generators. Then we train an embedding network collaboratively with multiple real instances in an open-set fashion. During training, the embedding network learns only real images and has no prior knowledge regarding AF images. In the testing phase, real and AF images are processed for detection. The proposed open-set CAF method can effectively detect AF images and is more robust against transferable updating.

Frame rate up-conversion (FRUC) is a technology for generating a higher frame rate video from one with a lower frame rate. The multi-view plus depth map (MVD) system has been recently considered a promising solution for three-dimensional video systems. Accordingly, an FRUC efficient solution for the MVD system is greatly needed. In this paper, we thus propose an FRUC approach that considers the MVD system characteristics. It divides a frame into multiple layers using a depth map, and it reconstructs occluded regions using neighboring views and depth map information. With reconstructed information, it accurately performs motion estimation when a region occlusion occurs. Experimental results demonstrated that the proposed method significantly improved the quality of interpolated frames, and it can effectively complement other conventional FRUC approaches.