Abstract: Digital forensic investigates evidence from electronic gadgets to aid in explaining criminal activities or security violations. The dynamic nature of technology constantly creates new kinds of digital devices and forms of data, challenging the acquisition and analysis of evidence. A new model named ArithmoGrad Optimization-based Modality Fusion with Attention Network (AG-MFAN) is proposed. ArithmoGrad Optimization (AG) improves deep convolutional neural networks (Deep CNN) by updating the feature extraction processes with arithmetic operations as well as gradients. It also adapts long short-term memory network parameters for temporal and sequential data of audio and documents to enhance the sequence modeling. The Modality Fusion with Attention Network then effectively combines these refined features using an advanced attention mechanism that prioritizes the most relevant information across different data types. This approach addresses the limitations of existing models, resulting in a more effective and precise system for detecting multimedia forgeries. The AG-MFAN model achieves the maximum accuracy, F1-score, precision, and recall of 96.76%, 96.76%, 96.40%, and 97.12% respectively for multi-modal analysis.

Key words: optimization, digital forensic investigation, deep convolutional neural network, long short-term memory, forgery detection