The JPEG-blockchain framework for GLAM services

This paper proposes a JPEG-blockchain framework for trusted media transaction. The new distributed and tamperproof framework intends to aid an emerging JPEG Privacy and Security standard. The blockchain network records any media transaction with necessary information related to intellectual property rights, access control rules and content signature. The content signature, generated by compressed sensed samples or low-resolution, low bit-rate compression is used to verify the image integrity and authenticity. We propose that every blockchian record, linked to a unique transaction hash, is encapsulated within the metadata contained in the JPEG box structure. As an example use case we have chosen the GLAM (Galleries, Libraries, Archives and Museums) sector due to its emerging need. This paper presents the proof of the concept and reports preliminary infrastructural development

WCBnet: Weighted convolutional block modelling of signed-value error levels for image-wise copy-move and splicing detection

Image manipulation which can easily generate hard-to-perceive fake information by image editing tools has become a threat of spreading visual mis/disinformation. With the speed and growth of such visual information presence in social media with respect to the current geopolitical affairs, tools for highly accurate verification of the authenticity of images are vital for AI-based fact checking. This work presents an efficient convolutional neural network (CNN) based approach for image manipulation detection. Our method, called WCBnet, starts with extracting learned features from the signed-value error levels (SEL) of compressed images on hierarchical convolution blocks. This is followed by adaptively concatenating, weighting and fusing these multi-level features by considering self-attention over all blocks according to different error levels corresponding to different manipulation types. We evaluate the performance of the proposed approach with respect to common manipulation datasets and compare with the state-of-the-art. WCBnet trained using around 2500 images of CASIA 2.0 dataset, resulted in the best F1-score for CASIA 1.0, Defacto, Coverage and Columbia datasets after fine-tuning by a small portion of those datasets. On average WCBnet improves the F1 score with respect to the second-best performing methods by 27.5%, 34.3%, 16.2% and 6.1% for these four datasets, respectively