A Hybrid CNN–LSTM Framework with Explainable AI for Robust Deepfake Detection

A Comprehensive Review of Deepfake Detection Techniques: From CNN-Based Models to Explainable Multimodal LLM Frameworks

An in-depth survey of fifteen state-of-art methodologies including classical CNN models, temporal-spatial video recognition, transformer-based networks, explainable AI (XAI) models, and models that combine multimodal large language model (LLM) products are provided.

CNN EfficientNetB0 Approach Based on Transfer Learning for Deepfake Detection and Disinformation Mitigation

Rapid advances in deep learning technology have led to the emergence of artificial intelligence (AI) media that is very similar to reality, called deepfakes, which have the potential to pose a serious threat to information integrity and public trust. Although detection methods using Convolutional Neural Networks (CNN) have been developed, most still struggle with generalization, particularly in distinguishing modern deepfakes from non-standard original images such as selfies, which often leads to high false positive rates. This study introduces a robust detection model based on the EfficientNetB0 architecture implemented through transfer learning techniques. To improve generalization capabilities and minimize bias, we compiled a large and balanced combined dataset by combining three different public datasets (including classic deepfakes, face swaps, and many authentic selfies). The model was trained using a two-stage strategy: first for feature extraction, then refinement with a very low learning rate. The model's performance was thoroughly evaluated on stratified test data using five key metrics. The results of the experiment showed outstanding performance, achieving 99.81% accuracy and a Macro F1 score of 99.81%. Additionally, the reliability metrics ROC-AUC, Average Precision (AP), and True Positive Rate (TPR) all reached 99.99%, while the False Positive Rate (FPR) remained strictly at 1%. As proof of concept, this optimized model was implemented in a web prototype built using the Django framework, allowing users to upload images and receive classification results in real-time.

Explainability-guided local–global fusion for video deepfake detection

A Novel X-Iv2 Ensemble Model for Deepfake Detection with Robustness Analysis and Explainable AI Approach

An ensemble-based approach, namely the X-Iv2 Ensemble approach, merging Inception ResNet v2 and Xception Net based on their complementary architectures to enhance feature extraction and classification is introduced, integrating Explainable AI (XAI) using Integrated Gradients to interpret decision-making processes.

Deepfakebuster: a confidence-calibrated adaptive ensemble framework for robust Deepfake image detection

DeepFakeBuster is presented as a confidence-calibrated adaptive ensemble for deepfake image detection by fusing together heterogeneous deep learning models built around detecting complementary forensic cues e.g., spatial inconsistencies, boundary artifacts, noise residuals, semantic consistency, and frequency-domain features.

Explainable Deep Learning Framework for AI-Generated Image Detection Using NASNet and Grad-CAM

This project presents an explainable deep learning framework for identifying real and AI-generated images using the NASNet architecture and achieves high detection accuracy while providing interpretable visual explanations, making it suitable for digital image verification, media authentication, and cybersecurity applications.