journal article·2024Open access

Advancing Deepfake Detection Using Xception Architecture: A Robust Approach for Safeguarding against Fabricated News on Social Media

Dunya Ahmed Alkurdi, Mesut Cevik, Abdurrahim Akgundogdu

Computers, Materials & Continua· Volume 81· Issue 3

Abstract

Deepfake has emerged as an obstinate challenge in a world dominated by light. Here, the authors introduce a new deepfake detection method based on Xception architecture. The model is tested exhaustively with millions of frame... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Digital Media Forensic Detection · Generative Adversarial Networks and Image Synthesis · Advanced Malware Detection Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (42)

Show source-provided references
1. DeepFake detection using transfer learning-based Xception model
2. Celeb-DF: A large-scale challenging dataset for deepfake forensics
3. Face2Face: Real-time face capture and reenactment of RGB videos
4. Evaluating the effectiveness of rationale-augmented convolutional neural networks for deepfake detection
5. DeeperForensics-1.0: A large-scale dataset for real-world face forgery detection
6. Deepfake video detection using recurrent neural networks
7. Detecting deep-fake videos from appearance and behavior
8. Analysis on capsule networks to detect forged images and videos
9. Multi-task learning for detecting and segmenting manipulated facial images and videos
10. Capsule-forensics: Using capsule networks to detect forged images and videos
11. Spatial-phase shallow learning: rethinking face forgery detection in frequency domain
12. Frequency-aware discriminative feature learning supervised by single-center loss for face forgery detection
13. FReTAL: Generalizing deepfake detection using knowledge distillation and representation learning
14. OC-FakeDect: Classifying deepfakes using one-class variational autoencoder
15. Dynamic texture analysis for detecting fake faces in video sequences10.1016/j.jvcir.2021.103239 ↗
16. Deepfake video detection through optical flow based CNN
17. Fake face detection via adaptive manipulation traces extraction network10.1016/j.cviu.2021.103170 ↗
18. MesoNet: A compact facial video forgery detection network
19. Detection of manipulated face videos over social networks: A large-scale study10.3390/jimaging7100193 ↗
20. Video face manipulation detection through ensemble of CNNs
21. Videoforensicshq: Detecting high-quality manipulated face videos
22. Detecting facial manipulated videos based on set convolutional neural networks10.1016/j.jvcir.2021.103119 ↗
23. Violence detection in videos using deep recurrent and convolutional neural networks
24. A video is worth more than 1000 lies. Comparing 3DCNN approaches for detecting deepfakes
25. FaceForensics++: Learning to detect manipulated facial images
26. DeepFake detection on publicly available datasets using modified AlexNet
27. Enhancing the fight against social media misinformation: An ensemble deep learning framework for detecting deepfakes
28. A review of deep learning-based approaches for deepfake content detection10.1111/exsy.13570 ↗
29. Deep learning based fake news detection on social media
30. Deep fake image classification engine using inception-ResNet-V1 network
31. Spatio-temporal catcher: A self-supervised transformer for deepfake video detection
32. Convolutional long short-term memory-based approach for deepfakes detection from videos10.1007/s11042-023-16196-x ↗
33. DeepFake detection based on high-frequency enhancement network for highly compressed content10.1016/j.eswa.2024.123732 ↗
34. An adversarial attack approach for eXplainable AI evaluation on deepfake detection models10.1016/j.cose.2023.103684 ↗
35. Deepfake detection using rationale-augmented convolutional neural network10.1007/s13204-021-02072-3 ↗
36. ImageNet classification with deep convolutional neural networks10.1145/3065386 ↗
37. Comparison of regularization methods for ImageNet classification with deep convolutional neural networks10.1016/j.aasri.2014.05.013 ↗
38. Biased dropout and crossmap dropout: Learning towards effective Dropout regularization in convolutional neural network10.1016/j.neunet.2018.03.016 ↗
39. Revisiting batch normalization for training low-latency deep spiking neural networks from scratch10.3389/fnins.2021.773954 ↗
40. Xception: Deep learning with depthwise separable convolutions
41. Composition-preserving deep approach to full-reference image quality assessment10.1007/s11760-020-01664-w ↗
42. Analysis survey on deepfake detection and recognition with convolutional neural networks