This research work aims to utilize deep learning techniques to identify autism traits in children based on their facial features. By combining traditional convolutional approaches with attention layers, the study seeks to enhance interpretability and accuracy in identifying autism spectrum disorder (ASD) traits. The dataset includes diverse facial images of children diagnosed with ASD and neuro-typical children, ensuring comprehensive representation. Preprocessing techniques standardize and enhance image quality, mitigating biases. Integration of attention layers within the convolutional neural net-work (CNN) architecture focuses on crucial facial features, improving feature extraction and classification accuracy. This approach enhances model interpretability through eXplainable AI (XAI) techniques. Model training involves optimization and validation processes, employing hyper parameter tuning and cross-validation for robustness. The performance of this combined model yielded close to 95% accuracy outperforming existing models in terms of complexity to accuracy ratio.

In the direct-to-home (DTH) environment video-on-demand (VOD) applications are tremendously popular due to its inexpensive and convenient nature.  In VOD approach legal customers can connect their set-top boxes (STB) to the Internet and can access or record the available content. Due to the easy transmission of the highest quality digital data to the customers by the pay-per-view approach, the data are highly at risk. The data can be vulnerable for illegal distribution of duplicate copies and they are prone to unnecessary modifications which creates a financial loss to the information creators. So it is necessary to authenticate the owner as well as the illegal distributor to reduce the digital piracy which is the motivation for this work. This paper presents a forensic watermarking scheme for protecting copyrights, and for identifying the illegal distributor who distributes the legal copy in the illegal fashion though it is copyright violation. In this paper, two watermarks are embedded in the video that is on-demand, where one watermark is the owner’s information and another watermark is related to the unique information of the STB.  This work is also suitable for the biomedical domain, where one watermark can be the patient information and another watermark will be the health center information, in order to secure the patient information and the hospital information.