This research focuses on the detection of deepfake images and videos for forensic analysis using deep learning techniques. It highlights the importance of preserving privacy and authenticity in digital media. The background of the study emphasizes the growing threat of deepfakes, which pose significant challenges in various domains, including social media, politics, and entertainment. Current methodologies primarily rely on visual features that are specific to the dataset and fail to generalize well across varying manipulation techniques. However, these techniques focus on either spatial or temporal features individually and lack robustness in handling complex deepfake artifacts that involve fused facial regions such as eyes, nose, and mouth. Key approaches include the use of CNNs, RNNs, and hybrid models like CNN-LSTM, CNN-GRU, and temporal convolutional networks (TCNs) to capture both spatial and temporal features during the detection of deepfake videos and images. The research incorporates data augmentation with GANs to enhance model performance and proposes an innovative fusion of artifact inspection and facial landmark detection for improved accuracy. The experimental results show near-perfect detection accuracy across diverse datasets, demonstrating the effectiveness of these models. However, challenges remain, such as the difficulty of detecting deepfakes in compressed video formats, the need for handling noise and addressing dataset imbalances. The research presents an enhanced hybrid model that improves detection accuracy while maintaining performance across various datasets. Future work includes improving model generalization to detect emerging deepfake techniques better. The experimental results reveal a near-perfect accuracy of over 99% across different architectures, highlighting their effectiveness in forensic investigations.

Unplanned power outages create major economic costs. To better predict and manage these events, we present a multilayer graph neural network (GNN) framework that captures spatial, short-term co-occurrence, and statistically enriched co-failure patterns using 7 years of Oklahoma Gas & Electric data from 347 substations. These relations are encoded with weighted graph convolutions and fused by attention into a unified representation. For predictive maintenance, the model flags substations requiring near-term intervention across 30-, 60-, and 180-day horizons, achieving a peak 30-day F1 score of 0.8935. The same representation supports resilience planning by clustering substations into eight risk groups, each with distinct incident rates and recovery times. The highest-risk group has over six times higher incident rates than the low-risk groups. Combining prediction and clustering in a single framework provides utilities with an integrated basis for scheduling maintenance, prioritizing inspections, and targeting grid-hardening investments to minimize outage impacts and enhance reliability.

The Tor browser is widely used for anonymity, providing layered encryption for enhanced privacy. Besides its positive uses, it is also popular among cybercriminals for illegal activities such as trafficking, smuggling, betting, and illicit trade. There is a need for Tor Browser forensics to identify its use in unlawful activities and explore its consequences. This research analyzes artifacts generated by Tor on Windows-based systems. The methodology integrates forensic techniques into incident responses per NIST SP (800-86), exploring areas such as registry, storage, network, and memory using tools like bulk-extractor, autopsy, and regshot. We propose an automated PowerShell script that detects Tor usage and retrieves artifacts with minimal user interaction. Finally, this research performs timeline analysis and artifact correlation for a contextual understanding of event sequences in memory and network domains, ultimately contributing to improved incident response and accountability.

The growing prevalence of AI-generated multimedia content, particularly deepfakes, has facilitated the spread of fake news, propaganda, identity theft, and undermined the importance of privacy and authenticity in digital media. Existing techniques often lack robustness in handling complex deepfake artifacts, especially those involving fused facial regions such as the eyes, nose, and mouth. This study addresses the challenges posed by deepfakes across domains such as social media, politics, and entertainment by proposing a hybrid deep learning framework for forensic analysis. The framework integrates convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and temporal convolutional networks (TCNs) to effectively capture both spatial and temporal features. Experimental results demonstrate a detection accuracy of 99% across diverse datasets, highlighting the model’s effectiveness in detecting deepfakes, even with intricate manipulations. This research advances the field of digital forensics by enhancing methodologies for multimedia verification, promoting trust, and safeguarding authenticity in the digital age.