PAttSBiL: an efficient deep fake video detection using integrated deep learning methodologies

Toward Efficient Fake Frame Detection in Video Using Deep Learning

Background: Deepfake technology is a major social concern due to the rapid development of Artificial Intelligence (AI), especially in machine learning (ML) and deep learning (DL). Deepfakes are artificially modified videos and images that effectively change a person’s facial features or expressions to misrepresent reality. These videos and images, often unnoticed by casual observers, present significant ethical, political, and social implications, as they can spread misinformation, damage reputations, and influence public perception. This study is an attempt to detect artificially modified videos by analyzing each frame using DL. We use ResNet-50 architecture, a well-known Convolutional Neural Network (CNN) model, to identify tampered videos. Methods: The system is trained using the Celebrity Deep Fake dataset, which includes numerous original and fake video samples. The model assesses whether each frame is original or tampered with after the videos have been split into frames. The system is tested and evaluated using standard metrics, including accuracy, precision, recall, and F1-score. Results: The model achieved 82.33% accuracy, 76.70% precision, 89.15% recall, and an F1-score of 82.46%. These results indicate that deepfake videos were correctly detected and that the model was efficient at identifying most real deepfake instances. In addition, the F1-score of 82.46% is further evidence of the model’s stability, as it ensures both high accuracy and coherence across numerous cases. Conclusions: The findings indicate that the proposed model works effectively compared to existing deepfake methods. In the future, we intend to use a richer dataset with more resources, which may further enhance the accuracy of the model.

Detection of Face-Swap Based Deepfake Videos Using Hybrid CNN-LSTM Architecture

The development of deepfake technologies due to breakthroughs in AI and deep learning allows producing highly realistic manipulated videos and audio, thus posing a threat to misinformation and digital security. Despite deepfake technology having several legitimate uses, including use in the media industry, its inappropriate use for distributing fake news, impersonation, and cyber attacks demands the development of reliable detection techniques. The current state-of-the-art approaches of detecting deepfakes mostly utilize spatial or temporal analysis based on CNNs and RNNs; however, most existing approaches fail to generalize well and are unable to recognize more complicated manipulations on a variety of different data sets. This paper aims at developing a novel multimodal approach to deepfake detection, integrating spatial, temporal, and audio features. The proposed system makes use of CNN-based architectures to extract spatial information from the input images, transformers to capture the temporal information, and Mel-frequency cepstral coefficients (MFCC) to analyze the audio data. These heterogeneous features are combined using attention-based learning to improve the classification accuracy. The proposed method was tested on various benchmarking datasets, including FaceForensics++, DeepFake Detection Challenge (DFDC), and Celeb-DF, yielding higher accuracy than current methods. Experimental results indicate that the combination of multimodal features enhances the detection capacity. The proposed model is highly efficient in addressing deepfake challenges in the digital forensic field.

Enhanced Detection OF Deepfakes Through Facial Expression Processing Using Deep Learning Networks

Efficient Spatiotemporal Deepfake Video Detection using a Lightweight CNN–LSTM Framework with Temporal Attention

Deepfake videos generated using modern deep learning techniques pose significant threats to digital media authenticity and public trust. These manipulated videos often appear highly realistic, making manual verification difficult. This paper proposes an efficient spatiotemporal deepfake detection framework that combines Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and a temporal attention mechanism. MobileNetV2 is utilized as a CNN model for spatial feature extraction in individual video frames. The extracted features are then subjected to the LSTM network for detection of temporal inconsistencies in the video frames. Furthermore, the use of the temporal attention mechanism is proposed for the identification of video frames where the level of manipulation is higher. The experimental results show the efficiency and accuracy of the proposed method in video forgery detection. The proposed method attains 96.1% detection accuracy and has less computational complexity compared to other methods. Ablation experiments confirm the contribution of each component, and robustness is evaluated under H.264 compression and adversarial perturbation conditions.

Deep Learning based DeepFake Image Detection for Enhancing Social Media Security

The rapid growth of deep learning technologies and social media has led to a massive amount of deepfake content. The deepfake media is often utilized for spreading misinformation, identity theft, falsification of data, spoofing, and hate speech. Studies indicate that over 96% of deepfake videos online are used for malicious purposes, with detection becoming increasingly critical as generation techniques evolve. Automatic deepfake detection is challenging because advances in content creation make generated content indistinguishable from the original. This paper presents deepfake detection using a deep convolutional neural network and a Long Short-Term Memory (LSTM) network. The DCNN captures spatial correlations and local connectivity in images, extracting facial features and micro-expressions across multiple hierarchical layers. In contrast, the LSTM is used to capture temporal dependencies and long-term correlations in deepfake images, particularly to analyze frame-to-frame inconsistencies and unnatural motion patterns that characterize synthetic media. The DCNN-LSTM achieves an improved accuracy of 95.5% compared with DCNN (95.1%) and LSTM (84.3%) for AVCeleb dataset.

Deep Learning Approaches for Deepfake Detection and Classification in Video Datasets: A Comprehensive Survey

The development of deepfakes is becoming a serious threat to multimedia security, therefore making the need for robust and efficient detection systems vital. In this paper, an in-depth review of deep learning approaches that have been developed towards the automatic detection of deepfake videos is carried out. This includes twenty research papers published within the period of 2023 to 2026, discussing deep fake video detectors that use Convolutional Neural Networks (CNN) models, transformer models, graph models, ensemble learning models, and hybrids. Spatial and temporal learning approaches adopted in the detection of facially manipulated videos are discussed. Nonetheless, despite numerous developments, current detection systems are facing challenges like computational complexity, poor generalization, and susceptibility to distortions, among others. Also, the use of a large annotated database further restricts the application. Some of the advancements made recently include vision transformers, graph neural networks, and optimization of ensemble models to enhance performance. The future research needs to concentrate more on lightweight and generalized deep learning models that can be scalable and interpretable. The paper offers a systematic review of recent advancements in deepfake video detection research.