Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions
Abstract
:1. Introduction
- A summary of the state-of-the art deepfake generation and detection techniques;
- An overview of fundamental deep learning architectures used as backbone in deepfake video detection models;
- A list of existing deepfake datasets contributing to the improvement of the performance, generalization and robustness of deepfake detection models;
- A discussion of the limitations of existing techniques, challenges, and research directions in the field of deepfake detection and mitigation.
2. Related Surveys
3. Deepfake Generation
3.1. Deepfake Manipulation Types
3.2. Deepfake Generation Techniques
4. Deepfake Detection
4.1. Deepfake Detection Clues
4.1.1. Detection Based on Spatial Artifacts
4.1.8. Detection Based on Spatial-Temporal Features
4.2. Deep Learning Models for Deepfake Detection
5. Datasets
6. Challenges and Future Directions
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Author | Title | Covered | Not Covered |
---|---|---|---|
Sudhakar and Shanthi [21] | Deepfake: An Endanger to Cyber Security | Deepfake generation | Deepfake types |
Deepfake detection | Datasets | ||
Salman et al. [22] | Deepfake Generation and Detection: Issues, Challenges, and Solutions | Audio–visual Deepfake generation | Datasets |
Deepfake detection | |||
Khder et al. [23] | Artificial Intelligence into Multimedia Deepfakes Creation and Detection | Deepfake types | Datasets |
Deepfake generation | |||
Deepfake detection | |||
Kandari et al. [24] | A Comprehensive Review of Media Forensics and Deepfake Detection Technique | Forensic-based deepfake detection methods | Deepfake types |
Deepfake generation | |||
Datasets | |||
Boutad**e et al. [25] | A comprehensive study on multimedia Deepfakes | Deepfake generation | Deepfake types |
Deepfake detection | |||
Datasets | |||
Threats and limitations | |||
Mallet et al. [26] | Using Deep Learning to Detecting Deepfakes | Deepfake detection | Deepfake generation |
Datasets | Deepfake types | ||
Limitations | |||
Das et al. [15] | A Survey on Deepfake Video-Detection Techniques Using Deep Learning | Deep learning-based detection models | Deepfake types |
Deepfake generation | |||
Datasets | |||
Alanazi [27] | Comparative Analysis of Deepfake Detection Techniques | Deepfake creation | Datasets |
Deepfake detection | |||
**nwei et al. [28] | An Overview of Face Deep Forgery | Deepfake generation | Deepfake detection |
Deepfake types | |||
Datasets | |||
Weerawardana and Fernando [29] | Deepfakes Detection Methods: A Literature Survey | Deepfake detection | Deepfake types |
Limitations | Deepfake generation | ||
P and Sk [30] | Deepfake Creation and Detection: A Survey | Deepfake generation | Deepfake types |
Deepfake detection | Datasets | ||
Lin et al. [16] | A Survey of Deepfakes Generation and Detection | Deepfake types | Future trends |
Deepfake generation | |||
Deepfake detection | |||
Datasets | |||
Khichi and Kumar Yadav [18] | A Threat of Deepfakes as a Weapon on Digital Platforms and their Detection Methods | Deepfake generation | Datasets |
Deepfake detection | |||
Limitations and future trends | |||
Chaudhary et al. [19] | A Comparative Analysis of Deepfake Techniques | Deepfake creation | Deepfake types |
Deepfake detection | |||
Future directions | |||
Datasets | |||
Zhang et al. [31] | Deep Learning in Face Synthesis: A Survey on Deepfakes | Deepfake types | Datasets |
Deepfake generation | Deepfake detection | ||
Younus and Hasan [20] | Abbreviated View of Deepfake Videos Detection Techniques | Deepfake generation | Deepfake types |
Deepfake detection | Datasets |
Author | Features | Technique | Intra-Dataset Performance (%) | Dataset |
---|---|---|---|---|
Zhao et al. [97] | Spatial temporal | Xception, Video Transformer | ACC (DF = 98.9 F2F = 96.1 FS = 97.5 NT = 92.1) | FF++(LQ) |
ACC (DF = 99.6 F2F = 99.6 FS = 100 NT = 96.8) | FF++(HQ) | |||
ACC = 99.8 | Celeb-DF | |||
ACC = 92.1 | DFDC | |||
Yu et al. [98] | Spatial temporal | Global Inconsistency View, Multi-timescale Local Inconsistency View | ACC = 98.86 AUC = 99.89 | FF++ |
ACC = 98.78 AUC = 99.81 | DFD | |||
ACC = 95.93 AUC = 98.96 | DFDC | |||
ACC = 99.64 AUC = 99.78 | Celeb-DF | |||
ACC = 98.94 AUC = 99.27 | DFR1.0 | |||
Yang, Z. et al. [99] | Attentional features from facial regions | 3D-CNN, TGCN, Spatial-temporal Attention, Masked Relation Learner | ACC = 91.81 | FF++(LQ) |
ACC = 93.82 | FF++(HQ) | |||
AUC = 99.96 | Celeb-DF | |||
AUC = 99.11 | DFDC | |||
Yang, W. et al. [62] | Audio-Visual Features | Temporal-Spatial Encoder, Multi-Modal Joint-Decoder | ACC = 95.3 AUC = 97.6 | DefakeAVMiT |
ACC = 83.7 AUC = 89.2 | FakeAVCeleb | |||
ACC = 91.4 AUC = 94.8 | DFDC | |||
Shang et al. [100] | Spatial temporal | Temporal convolutional network, Spatial Relation Graph Convolution Units, Temporal Attention Convolution Units | ACC (DF = 99.29 F2F = 97.14 FS = 100 NT = 95.36) | FF++(HQ), Celeb-DF, DFDC |
Rajalaxmi et al. [101] | Spatial inconsistencies | Inception-ResNet-V2 | ACC = 98.37 | DFDC |
Korshunov et al. [102] | Spatial temporal | Xception | ACC = 100.00 | Celeb-DF |
ACC = 99.14 | FF++ | |||
AUC = 99.93 | DFR1.0 | |||
AUC = 96.57 | HifiFace | |||
Patel et al. [103] | Temporal inconsistencies | Dense CNN | ACC = 97.2 | CelebA, FFHQ, GDWCT, AttGAN, STARGAN, StyleGAN, StyleGAN2 |
Pang et al. [104] | Spatial temporal | Bipartite Group Sampling, Inconsistency Excitation, Longstanding Inconsistency Excitation, | ACC = 85.61 AUC = 91.23 | WildDeepfake |
ACC = 97.76 AUC = 99.57 | FF++(HQ) | |||
ACC = 91.60 AUC = 96.55 | FF++(LQ) | |||
ACC = 97.35 AUC = 99.75 | DFDC | |||
Mehra et al. [105] | Spatial temporal | 3D-Residual-in-Dense Net | ACC (DF = 98.57 F2F = 97.84 FS = 94.62 NT = 96.05) | FF++ |
AUC= 92.93 | Celeb-DF | |||
Lu et al. [81] | Spatial temporal | VGG Capsule Networks | ACC = 94.07 | Celeb-DF, FF++ |
Liu et al. [73] | Identity information | Encoder, RNN | AUC (FF++ = 99.95) | FF++, DFD, DFR1.0, Celeb-DF |
Lin et al. [106] | Face semantic information | EfficientNet-b4 ViT | AUC = 99.80 | Celeb-DF |
AUC = 88.47 | DFDC | |||
ACC = 90.74 AUC = 94.86 | FF++(LQ) | |||
ACC = 82.63 | WildDeepfake | |||
Liang et al. [53] | Facial geometry features | Facial geometry prior module, CNN-LSTM | ACC = 99.60 | FF++ |
ACC = 97.00 | DFR1.0 | |||
ACC = 82.84 | Celeb-DF | |||
ACC = 94.68 | DFD | |||
Khalid et al. [107] | Spatial inconsistencies | Swin Y-Net Transformers | ACC (DF = 97.12 F2F = 95.73 FS = 92.10 NT = 79.90) | FF++ |
AUC (DF = 97.00 F2F = 97.00 FS = 93.00 NT = 83.00) | ||||
ACC = 97.91 AUC = 98.00 | Celeb-DF | |||
Chen et al. [65] | Bi-granularity artifacts | ResNet-18decoder | Celeb-DF AUC = 99.80 FF++ AUC = 99.39 | Celeb-DF, FF++ DFD, DFDC-P, UADFV, DFTIMIT, WildDeepfake |
Agarwal et al. [70] | Identity information | Action Units | AUC = 97.00 | World Leaders Dataset, Wav2Lip, FaceSwap YouTube |
Cai et al. [61] | Audio-visual inconsistencies | 3DCNN 2DCNN | ACC = 99.00 | LAV-DF |
ACC = 84.60 | DFDC | |||
Zhuang et al. [108] | Spatial inconsistencies | Vision Transformer | FF++ AUC = 99.33 | FF++, Celeb-DF, DFD, DFDC |
Yan et al. [109] | Spatial temporal frequency features | GNN | AUC = 91.90 ACC = 89.70 | FF++(LQ) |
AUC = 99.50 ACC = 97.80 | F++(HQ) | |||
Saealal et al. [110] | Spatial temporal | VGG11 | AUC = 0.9446 | OpenForensics |
Xu et al. [111] | Spatial inconsistencies | Supervised contrastive model, Xception | ACC = 93.47 | FF++ |
**a et al. [112] | Image texture | MesoNet | ACC = 94.10 AUC = 97.40 | FF++ |
ACC = 94.90 AUC = 94.30 | Celeb-DF | |||
AUC = 96.50 | UADFV | |||
AUC = 84.30 | DFD | |||
Wu, N. et al. [113] | Semantic features | Multisemantic path neural network | ACC = 76.31 | FF++(LQ) |
ACC = 94.21 | F++(HQ) | |||
AUC = 99.52 | TIMIT(LQ) | |||
AUC = 99.12 | TIMIT(HQ) | |||
Wu, H. et al. [114] | Spatial inconsistencies | Multistream Vision Transformer Network | ACC = 89.04 | FF++(LQ) |
ACC = 99.31 | FF++(HQ) | |||
Waseem et al. [82] | Spatial temporal | XceptionNet and 3DCNN | FF++ ACC (DF = 95.55 F2F = 77.05 NT = 75.35) | FF++, DFTIMIT, DFD |
Cozzolino et al. [115] | Audio-visual inconsistencies | ResNet-50 | Avg AUC = 94.6 | DFDC, DFTIMIT, FakeAVCeleb, KoDF |
Wang, J. et al. [57] | Spatial-frequency domain | Multi-modal Multi-scale Transformers | ACC = 92.89 AUC = 95.31 | FF++(LQ) |
ACC = 97.93 AUC = 99.51 | FF++(HQ) | |||
AUC = 99.80 | Celeb-DF | |||
AUC = 91.20 | SR-DF | |||
Wang, B. et al. [116] | Image grey space features | CNN Siamese network | ACC (DF = 84.14 F2F = 98.62 FS = 99.49 NT = 98.90) | FF++(LQ) |
ACC (DF = 95.79 F2F = 97.12 FS = 97.37 NT = 84.71) | FF++(HQ) | |||
Saealal et al. [117] | Biological signals (Eye blinking) | Cascade CNN-LSTM-FCNs | ACC (DF = 94.65 F2F = 90.37 FS = 91.54 NT = 86.76) | FF++ |
Dataset | Year Released | Real Content | Fake Content | Generation Method | Modality |
---|---|---|---|---|---|
FaceForensics ++ [118] | 2019 | 1000 | 4000 | DeepFakes [119], Face2Face2 [37], FaceSwap [120], NeuralTextures [121], FaceShifter [34] | Visual |
Celeb-DF (v2) [122] | 2020 | 590 | 5639 | DeepFake [122] | Visual |
DFDC [123] | 2020 | 23,654 | 104,500 | DFAE, MM/NN, FaceSwap [120], NTH [124], FSGAN [125] | Audio/Visual |
DeeperForensics-1.0 [126] | 2020 | 48,475 | 11,000 | DF-VAE [126] | Visual |
WildDeepfake [127] | 2020 | 3805 | 3509 | Curated online | Visual |
OpenForensics [128] | 2021 | 45,473 | 70,325 | GAN based | Visual |
KoDF [129] | 2021 | 62,166 | 175,776 | FaceSwap [120], DeepFaceLab [51], FSGAN [125], FOMM [130], ATFHP [131], Wav2Lip [132] | Visual |
FakeAVCeleb [133] | 2021 | 500 | 19,500 | FaceSwap [120], FSGAN [125], SV2TTS [134], Wav2Lip [132] | Audio/Visual |
DeepfakeTIMIT [135] | 2018 | 640 | 320 | GAN based | Audio/Visual |
UADFV [136] | 2018 | 49 | 49 | DeepFakes [119] | Visual |
DFD [137] | 2019 | 360 | 3000 | DeepFakes [119] | Visual |
HiFiFace [138] | 2021 | - | 1000 | HifiFace [138] | Visual |
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Share and Cite
Naitali, A.; Ridouani, M.; Salahdine, F.; Kaabouch, N. Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions. Computers 2023, 12, 216. https://doi.org/10.3390/computers12100216
Naitali A, Ridouani M, Salahdine F, Kaabouch N. Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions. Computers. 2023; 12(10):216. https://doi.org/10.3390/computers12100216
Chicago/Turabian StyleNaitali, Amal, Mohammed Ridouani, Fatima Salahdine, and Naima Kaabouch. 2023. "Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions" Computers 12, no. 10: 216. https://doi.org/10.3390/computers12100216