Digital forensics is becoming increasingly reliant on advanced computational methodologies to keep pace with the evolving landscape of digital crime. The rise of deep learning and machine learning has significantly improved various aspects of forensic investigations, particularly in image and video analysis. However, the implementation of state-of-the-art deep learning models requires substantial computational resources, specifically GPU-based processing capabilities. This proposal seeks to secure GPU resources to develop and deploy sophisticated deep learning models for critical digital forensics tasks, including image manipulation detection, criminal activity analysis, and image restoration techniques like inpainting and watermark removal. The project seeks support for a research team of 5-7 members to conduct advanced research in digital forensics with a focus on deep learning methodologies. Our aim is to enhance forensic capabilities in three key areas: 1) Image Manipulation Detection; 2) Criminal Activity Observation; and 3) Image Restoration. The primary goal is to achieve reliable detection of tampered digital evidence, improve video-based observation of criminal activities, and restore images effectively when they are obstructed by intentional or accidental marks. Image Manipulation Detection: With the proliferation of sophisticated photo-editing tools, detecting digital forgeries has become a significant challenge. This project will employ convolutional neural networks (CNNs) and generative adversarial networks (GANs) to identify discrepancies and anomalies that arise during image tampering. By training models on large datasets of authentic and manipulated images, our proposed solution aims to reliably distinguish between authentic and altered visual content. This will strengthen the integrity and authenticity of digital evidence in forensic investigations. Criminal Activity Observation: Deep learning-based image and video analysis can considerably enhance the detection of suspicious or criminal activities. The project will leverage advanced techniques such as action recognition models and object detection frameworks to analyze surveillance footage. These models, built on cutting-edge neural architectures like YOLO and 3D CNNs, will help identify illegal activities, suspicious behaviors, and interactions that traditional investigations might overlook. Image Restoration – Inpainting and Watermark Removal: Image inpainting seeks to reconstruct missing or damaged regions in an image, restoring it to its original state. Additionally, this project will focus on developing algorithms for watermark removal, especially in cases where key details are obscured. GAN-based approaches, which intelligently recreate missing content by analyzing contextual information, will be applied to both image inpainting and watermark removal tasks. The goal is to maintain the authenticity of restored evidence while preserving the original content. The successful execution of this project could provide law enforcement agencies and cybersecurity professionals with powerful new tools to address digital crimes effectively. By automating the detection of image tampering, improving the accuracy of criminal activity observation in surveillance footage, and offering reliable image restoration techniques, these tools would significantly enhance the efficiency and effectiveness of forensic investigations. Furthermore, the methodologies developed through this research could contribute to the broader field of digital forensics by setting new standards for accuracy, scalability, and reliability in handling digital evidence.