Deepfake Detection using Mamba

The rapid proliferation of deepfake technology, driven by advanced generative models like GANs and Diffusion Models, has enabled the creation of hyper-realistic manipulated media that threatens information integrity, personal reputation, and national security. As these tools become more accessible, the need for automated, robust, and efficient detection systems is critical. Existing detection methodologies largely rely on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), or Vision Transformers (ViTs). However, each approach faces significant limitations. CNN-based methods suffer from "temporal blindness," failing to detect inconsistencies across video frames. Hybrid CNN-RNN models address this but are hindered by slow training speeds and limited capacity for modeling long-range dependencies. While Transformer-based models excel at capturing global context, their quadratic computational complexity (O(N2)) makes them computationally expensive and memory-intensive, rendering them inefficient for processing long, high-resolution video sequences. Furthermore, many current models struggle to generalize effectively to unseen datasets and novel manipulation techniques. To overcome these challenges, this paper proposes a novel hybrid deepfake detection framework that integrates an EfficientNet backbone with the Mamba Selective State Space Model (SSM). This architecture leverages EfficientNet for robust spatial feature extraction and utilizes Mamba for temporal sequence modeling. Mamba introduces a content-aware selection mechanism and operates with linear-time complexity (O(N2)), allowing it to efficiently capture subtle long-range temporal inconsistencies that traditional models miss, without the heavy computational cost of Transformers. Experimental results demonstrate that this proposed system offers a scalable, efficient solution with superior generalization capabilities, making it highly suitable for real-time deepfake detection in diverse and complex video environments. Keywords: Deepfake Detection, GAN, Selective State Space Models (SSM), Mamba model, Vision Mamba (Vim), Temporal Sequence Modeling, EfficientNet Backbone, machine learning, deep learning, convolutional neural networks, transformers, attention mechanisms, benchmarking systems, datasets, Synthetic Media Detection, Linear-Time Complexity, Video Anomaly Detection, Computational Efficiency, Hybrid CNN-SSM, Facial Forgery Detection, Long-Range Dependency Modeling.