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Review

Deepfakes Generation and Detection: A Short Survey

by
Zahid Akhtar
Department of Network and Computer Security, State University of New York (SUNY) Polytechnic Institute, Utica, NY 13502, USA
J. Imaging 2023, 9(1), 18; https://doi.org/10.3390/jimaging9010018
Submission received: 20 November 2022 / Revised: 5 January 2023 / Accepted: 9 January 2023 / Published: 13 January 2023
(This article belongs to the Special Issue Deepfakes, Fake News and Multimedia Manipulation from Generation to Detection)
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Abstract

:
Advancements in deep learning techniques and the availability of free, large databases have made it possible, even for non-technical people, to either manipulate or generate realistic facial samples for both benign and malicious purposes. DeepFakes refer to face multimedia content, which has been digitally altered or synthetically created using deep neural networks. The paper first outlines the readily available face editing apps and the vulnerability (or performance degradation) of face recognition systems under various face manipulations. Next, this survey presents an overview of the techniques and works that have been carried out in recent years for deepfake and face manipulations. Especially, four kinds of deepfake or face manipulations are reviewed, i.e., identity swap, face reenactment, attribute manipulation, and entire face synthesis. For each category, deepfake or face manipulation generation methods as well as those manipulation detection methods are detailed. Despite significant progress based on traditional and advanced computer vision, artificial intelligence, and physics, there is still a huge arms race surging up between attackers/offenders/adversaries (i.e., DeepFake generation methods) and defenders (i.e., DeepFake detection methods). Thus, open challenges and potential research directions are also discussed. This paper is expected to aid the readers in comprehending deepfake generation and detection mechanisms, together with open issues and future directions.

1. Introduction

It is estimated that 1.8 billion images and videos per day are uploaded to online services, including social and professional networking sites [1]. However, approximately 40% to 50% of these images and videos appear to be manipulated [2] for benign reasons (e.g., images retouched for magazine covers) or adversarial purposes (e.g., propaganda or misinformation campaigns). In particular, human face image/video manipulation is a serious issue menacing the integrity of information on the Internet and face recognition systems since faces play a central role in human interactions and biometrics-based person identification. Therefore, plausible manipulations in face samples can critically subvert trust in digital communications and security applications (e.g., law enforcement).
DeepFakes refer to multimedia content that has been digitally altered or synthetically created using deep learning models [3]. Deepfakes are the results of face swapping, enactment/animation of facial expressions, and/or digitally generated audio or non-existing human faces. In contrast, face manipulation involves modifying facial attributes such as age, gender, ethnicity, morphing, attractiveness, skin color or texture, hair color, style or length, eyeglass, makeup, mustache, emotion, beard, pose, gaze, mouth open or closed, eye color, injury and effects of drug use [4,5], and adding imperceptible perturbations (i.e., adversarial examples), as shown in Figure 1. The readily-available face editing apps (e.g., FaceApp [6], ZAO [7], Face Swap Live [8], Deepfake web [9], AgingBooth [10], PotraitPro Studio [11], Reface [12], Audacity [13], Soundforge [14], Adobe Photoshop [15]), and Deep Neural network (DNN) source codes [16,17] have enabled even non-experts and non-technical people to create sophisticated deepfakes and altered face samples, which are difficult to be detected by human examiners and current image/video analysis forensics tools.
Deepfakes are expected to advance present disinformation and misinformation sources to the next level, which could be exploited by trolls, bots, conspiracy theorists, hyperpartisan media, and foreign governments; thus, deepfakes could be fake news 2.0. Deepfakes can be used for productive applications such as realistic dubbing of foreign video films [18] or historical figure reanimation for education [19]. Deepfakes can also be used for destructive applications such as the use of fake pornographic videos to damage a person’s reputation or to blackmail them [20], manipulating elections [21], creating warmongering situations [22], generating political or religious unrest via fake speeches [23], causing chaos in financial markets [24], or identity theft [25]. It is easy to notice that the number of malevolent exploitations of deepfakes chiefly dominates the benevolent ones. In fact, not only have recent advances made creating a deepfake with just a still image [26], but also deepfakes are successfully being misused by cybercriminals in the real world. For instance, an audio deepfake was utilized to scam a CEO out of $243,000 [27]. The issue of deepfakes and face manipulations is getting compounded as they can negatively affect the automated face recognition system (AFRS). For instance, studies have shown that AFRS’s error rates can reach up to 95% under deepfakes [28], 50–99% under morphing [29], 17.08% under makeup manipulation [30], 17.05–99.77% under partial face tampering [31], 40–74% under digital beautification [32], 93.82% under adversarial examples [33], and 67% under GANs generated synthetic samples [34]. Similarly, automated speaker verification’s accuracy drops to 40% from 98% under adversarial examples [35].
There exist many deepfake and face manipulation detection methods. However, a systematic analysis shows that the majority of them have low generalization capability, i.e., their performances drop drastically when they encounter a novel deepfake/manipulation type that was not used during the training stage, as also demonstrated in [36,37,38,39,40]. Also, prior studies considered deepfake detection a reactive defense mechanism and not as a battle between the attackers (i.e., deepfake generation methods) and the defenders (i.e., deepfake detection methods) [41,42,43]. Therefore, there is a crucial gap between academic deepfake solutions and real-world scenarios or requirements. For instance, the foregoing works are usually lagging in the robustness of the systems against adversarial attacks [44], decision explainability [45], and real-time mobile deepfake detection [46].
The study of deepfake generation and detection, in recent years, is gathering much more momentum in the computer vision and machine learning community. There exist some review papers on this topic (e.g., [5,24,47,48]), but they are focused mainly on deepfake or synthetic samples using generative adversarial networks. Moreover, most survey articles (e.g., [4,49,50]) were mainly written from an academic point of view and not from a practical development point of view. Also, they did not cover the advent of very recent face manipulation methods and new deepfake generation and detection techniques. Thus, this paper provides a concise but comprehensive overview from both theoretical and practical points of view to furnish the reader with an intellectual grasp as well as to facilitate the progression of novel and more resilient techniques. For example, publicly available apps, codes, or software information can be easily accessed or downloaded for further development and use. All in all, this paper presents an overview of current deepfake and face manipulation techniques by covering four kinds of deepfake or face manipulation. The four main types of manipulation are identity swap, face reenactment, attribute manipulation, and entire face synthesis, where every category manipulation generation and such manipulation detection methods are summarized. Furthermore, open challenges and potential future directions (e.g., robust deepfake detection systems against adversarial attacks using multistream and filtering schemes) that need to be addressed in this evolving field of deepfakes are highlighted. The main objectives of this article are to complement earlier survey papers with recent advancements, to impart to the reader a deeper understanding of the deepfake creation and detection domain, and to use this article as ground truth to develop novel algorithms for deepfake and face manipulation generation and detection systems.
The rest of the article is organized as follows. Section 2 presents deepfake and face manipulation generation as well as detection techniques. In Section 3, the open issues and potential future directions of deepfake generation and detection are discussed. The conclusions are described in Section 4.

2. Deepfake Generation and Detection

We can broadly define deepfake as “believable audio-, visual- or multimedia generated by deep neural networks”. Deepfake/face manipulation can be categorized into four main groups: identity swap, face reenactment, attribute manipulation, and entire face synthesis [47], as shown in Figure 2. Several works have been conducted on different types of deepfake/face manipulation generation and detection. However, in the following subsections, we have included representative studies based on their novelty, foundational idea, and/or performance. Also, studies have been incorporated to represent the most up-to-date research works depicting the state-of-the-art in deepfake generation and detection.

2.1. Identity Swap

Here, an overview of existing identity swap or face swap (i.e., replacing a person’s face with another person’s face) generation and detection methods is presented.

2.1.1. Identity Swap Generation

This consists of replacing the face of a person in the target image/video with the face of another person in the source image/video [51]. For example, Korshunova et al. [52] developed a face-swapping method using Convolutional Neural Networks (CNNs). While Nirkin et al. [53] proposed a technique using a standard fully convolutional network in unconstrained settings. Mahajan et al. [54] presented a face swap procedure for privacy protection. Wang et al. [55] presented a real-time face-swapping method. Natsume et al. [56] proposed a region-separative generative adversarial network (RSGAN) for face swapping and editing. Other interesting face swamping methods can be seen in [28,57,58,59,60,61].

2.1.2. Identity Swap Detection

Ample studies have been conducted on identity swap deepfake detection. For instance, Koopman et al. [62] analyzed photo response non-uniformity (PRNU) for detection. Also, warping artifacts [63], eye blinking [64], optical flow with CNNs [65], heart rate [66], image quality [28], local image textures [37], long short-term memory (LSTM) and recurrent neural network (RNN) [67], multi-LSTM and blockchain [68], clustering [69], context [70], compression artifacts [71], metric learning [72], CNN ensemble [73], Identity-aware [74], transformers [75], audio-visual dissonance [76], and multi-attentional [77] features were used. Very few works have been focused on deepfake detection method’s explainability (e.g., [78]) and generalization capability (e.g., work of Bekci et al. in [38] and Aneja et al. [79] work using zero-shot learning). Recently, S. Liu et al. [80] proposed a block shuffling learning method to detect deepfakes, where the image is divided into blocks, and using random shuffling where intra-block and inter-block-based features are extracted.

2.2. Face Reenactment

Here, an overview of prior face reenactment (i.e., changing the facial expression of the individual) generation and detection techniques is provided.

2.2.1. Face Reenactment Generation

This consists of replacing the facial expression of a person in the target image/video with the facial expression of another person in the source image/video [47]. It is also known as expression swap or puppet master. For instance, Thies et al. [82] developed real-time face reenactment RGB video streams. Whereas encoder-decoder, RNN, unified landmark converter with geometry-aware generator, GANs, and task-agnostic GANs-based schemes were designed by Kim et al. [83], Nirkin et al. [84], Zhang et al. [85], Doukas et al. [86], and Cao et al. [87], respectively.

2.2.2. Face Reenactment Detection

Face reenactment detection methods were designed by Cozzolino et al. [88] using CNNs; Matern et al. [89] using visual features with logistic regression and MLP; Rossler et al. [90] using mesoscopic, steganalysis, and CNN features; Sabir et al. [91] using RNN; Amerini et al. [65] using Optical Flow + CNNs; Kumar et al. [92] using multistream CNNs; and Wang et al. [93] using 3DCNN. In contrast, Zhao et al. [94] designed a spatiotemporal network, which can utilize complementary global and local information. In particular, the framework uses a spatial module for the global information, and the local information module extracts features from patches selected by attention layers.

2.3. Attribute Manipulation

Here, an overview of existing attribute manipulation or face retouching, or face editing (i.e., altering certain face attributes such as skin tone, age, and gender) generation and detection techniques is presented.

2.3.1. Attribute Manipulation Generation

This consists of modifying some facial attributes, e.g., color of hair/skin, gender, age, adding glasses [95,96,97]. It is also known as face editing or face retouching. Xiao et al. [98] presented a multi-attribute manipulation GANs-based system. Moreover, spatial attention in GANs [99], variational autoencoder (VAE) + GANs [100], multi-domain GANs [101], geometry-aware GANs [102], mask-guided GANs [103], 3D face morphable model [104], and GIMP animation [105] based methods have been designed.

2.3.2. Attribute Manipulation Detection

In [36], authors studied the efficacy of different deep learning models’ efficacy for attribute manipulation detection. The Deep Boltzmann machine by Bharati et al. [106], CNN by Dang et al. [107], LBP + landmarks + CNNs by Rathgeb et al. [108], adaptive manipulation traces by Guo et al. [109], encoder-decoder by Mazaheri et al. [110], facial boundary features by Kim et al. [111], and PRNU by Scherhag et al. [112] were exploited.

2.4. Entire Face Synthesis

Here, an overview of prior entire face synthesis (i.e., creating non-existent face samples) generation and detection techniques is provided.

2.4.1. Entire Face Synthesis Generation

This consists of generating entire non-existent face images [113,114,115]. Berthelot et al. [116] developed boundary equilibrium GANs to create synthetic faces. Similarly, various approaches have been devised, e.g., coupled GANs [117], invertible convolution [118], U-Net [119], from speech to face GANs [120], high-resolution deep convolutional GANs [121], interactive anycost GANs [122], and structured disentanglement framework for face generation and editing [123].

2.4.2. Entire Face Synthesis Detection

Many studies have also focused on entire face synthesis detection. For example, McCloskey et al. [124] presented a color cues-based system. While GAN fingerprint + CNNs [125], PRNU [126], co-occurrence matrices [127], neuron behaviors [128], incremental learning + CNNs [129], and self-attention mechanism [130] were also utilized. Table 1 presents a summary of deepfake and face manipulation generation and detection techniques. Guo et al. [131] showed that GANs-generated faces could be detected by analyzing the irregular pupil shapes, which may be caused by the lack of physiological constraints in the GANs models.

3. Open Issues and Research Directions

Although great efforts have been made in devising deepfake generation and detection, there are several issues yet to be addressed successfully. In the following, some of them are discussed.

3.1. Generalization Capability

It is easy to notice in the literature that most of the existing deepfake detection frameworks’ performances decrease remarkably when tested under deepfakes, manipulations, or databases that were not used for the training. Thus, detecting unknown novel deepfakes or deepfake generation tools is yet a big challenge. The generalization capability of deepfake detectors is vital for dependable precision and public trust in the information being shared online. Some preliminary generalization solutions have been proposed, but their ability to tackle novel emerging deepfakes is still an open issue.

3.2. Explainability of Deepfake Detectors

There is a lack of work on the deepfake detection framework’s interpretability and dependability. Most deep-learning-based deepfake or face manipulation detection methods in the literature usually do not explain the reason behind the final detection outcome. It is mainly due to deep learning techniques being the black box in nature. Current deepfake or face manipulation detectors only give a label, confidence percentage, or fakeness probability score but not the insight description of results. Such a description would be useful to know why the detector made a certain decision. Also, deepfake or face manipulation (e.g., applying digital makeup) can be performed either for benign or malicious intentions. Nonetheless, present deepfake or face manipulation detection techniques cannot distinguish the intent. For deepfake detection framework’s interpretability and dependability, various advanced combinations of techniques such as fuzzy inference systems [187], layer-wise relevance propagation [188], and the Neural Additive Model [189] could be helpful.

3.3. Next-Generation Deepfake and Face Manipulation Generators

Improved deepfake and face manipulation generation techniques will help develop more advanced and generalized deepfake detection methods. Some of the shortcomings of current datasets and generation methods are the lack of ultra-high-resolution samples (e.g., existing methods are usually generating 1014 × 1024 resolution samples, which is not sufficient for the next generation of deepfakes), limited face attribution manipulations (i.e., face attribute manipulation types are dependent on the training set, thereby manipulation characteristics and attributes are limited, and novel attributes cannot be generated), video continuity problem (i.e., the deepfake/face manipulation, especially identity swap, techniques neglects the continuation of video frames as well as physiological signals), and no obvious deepfake/face manipulations (i.e., present databases are not composed of obvious fake samples such as a human face with three eyes).

3.4. Vulnerability to Adversarial Attacks

Recent studies have shown that deep learning-based deepfake and face manipulation detection methods are vulnerable to adversarial examples [44]. Though current detectors are capable of handling several degradations (e.g., compression and noise), their accuracy goes to extremely low levels under adversarial attacks. Thus, next-generation techniques should be not only able to tackle deepfakes but also adversarial examples. To this aim, developing various multistream and filtering schemes could be effective.

3.5. Mobile Deepfake Detector

The neural networks-based deepfake detection methods, which are capable of attaining remarkable accuracy, are mostly unsuited for mobile platforms/applications owing to the huge number of parameters and computational cost. Compressed, yet effective, deep learning-based detection systems, which could be used on mobile and wearable devices, will greatly help counteract deepfakes and fake news.

3.6. Lack of Large-Scale ML-Generated Databases

Most studies on AI-synthesized face sample detection compiled their own database with various GANs. Thereby, different published studies have different performances on GANs samples, because the quality of GANs-generated samples varies and are mostly unknown. Several public GANs-generated fake face sample databases should be produced to help the advancement of this demanding research field.

3.7. Reproducible Research

In machine learning and the deepfake research community, the reproducible results trend should be urged by furnishing the public with large datasets with larger human scores/reasons, experimental setups, and open-source tools/codes. It will surely aid in outlining the true progress in the field and avoid overestimation of the performances by the developed methods.

4. Conclusions

AI-synthesized or digitally manipulated face samples, commonly known as DeepFakes, are a significant challenge threatening the dependability of face recognition systems and the integrity of information on the Internet. This paper provides a survey on recent advances in deepfake and facial manipulation generation and detection. Despite noticeable progress, there are several issues remaining to be resolved to attain highly effective and generalized generation and defense techniques. Thus, this article discussed some of the open challenges and research opportunities. The field of deepfakes still has to go a long way for dependable deepfake and face manipulation detection frameworks, which will need interdisciplinary research efforts in various domains, such as machine learning, computer vision, human vision, psychophysiology, etc. All in all, this survey may be utilized as a ground truth for developing novel AI-based algorithms for deepfake generation and detection. Also, it is hoped that this survey paper will motivate budding scientists, practitioners, researchers, and engineers to consider deepfakes as their domain of study.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data sharing not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Examples of different face manipulations: original samples (first row) and manipulated samples (second row).
Figure 1. Examples of different face manipulations: original samples (first row) and manipulated samples (second row).
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Figure 2. Real and fake examples of each deepfake/face manipulation group. The fake sample in “Entire face synthesis” group is obtained from the method in [81].
Figure 2. Real and fake examples of each deepfake/face manipulation group. The fake sample in “Entire face synthesis” group is obtained from the method in [81].
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Table
Table 1. Representative works on deepfake and face manipulation generation and detection techniques. SWR = successful swap rate; MS-SSIM = multi-scale structural similarity; Acc = accuracy; LL = Logloss; AUC = area under the curve; CL = contextual loss; RMSE = root mean square error; AU = Facial action unit; CSIM = Cosine Similarity between IMage embeddings; EER = Equal error rate; FID = Frechet inception distance; AP = Average Precision; KID = kernel inception distance; PSNR = Peak Signal-to-Noise Ratio.
Table 1. Representative works on deepfake and face manipulation generation and detection techniques. SWR = successful swap rate; MS-SSIM = multi-scale structural similarity; Acc = accuracy; LL = Logloss; AUC = area under the curve; CL = contextual loss; RMSE = root mean square error; AU = Facial action unit; CSIM = Cosine Similarity between IMage embeddings; EER = Equal error rate; FID = Frechet inception distance; AP = Average Precision; KID = kernel inception distance; PSNR = Peak Signal-to-Noise Ratio.
StudyApproachDatasetPerformanceSource CodeYear
Deepfake Generation
Wang et al. [55]Real-time face swapping using CANDIDE-3COFW [132], 300W [133], LFW [134]SWR = 87.9%.×2018
Natsume et al. [56]Face swapping and editing using RSGANCelebA [135]MS-SSIM = 0.087×2018
Chen et al. [61]High fidelity encoder-decoderVGGFace2 [136]Qualitative
Analysis		https://github.com/neuralchen/SimSwap (accessed on 4 January 2023)2021
Xu et al. [137]Lightweight Identity-aware Dynamic NetworkVGGFace2 [136]
FaceForensics++ [90]		FID = 6.79%https://github.com/Seanseattle/MobileFaceSwap (accessed on 4 January 2023)2022
Shu et al. [138]Portrait, identity, and pose encoders with generator and feature pyramid networkVoxCeleb2 [139]PSNR = 33.26https://github.com/jmliu88/heser (accessed on 4 January 2023)2022
Deepfake Detection
Afcha et al. [140]CNNsFaceForensics++ [90]Acc = 98.40%https://github.com/DariusAf/MesoNet (accessed on 4 January 2023)2018
Zhao et al. [77]Multi-attentional FaceForensics++ [90]
DFDC [3]		Acc = 97.60%
LL = 0.1679		https://github.com/yoctta/multiple-attention (accessed on 4 January 2023)2021
Miao et al. [141]Transformers via bag-of-feature for generalizationFaceForensics++ [90],
Celeb-DF [142], DeeperForensics-1.0 [143]		Acc = 87.86%
AUC = 82.52%
Acc = 97.01%		×2021
Prajapati et al. [144]Perceptual Image Assessment + GANsDFDC [3]AUC = 95%
Acc = 91%		https://github.com/pratikpv/mri_gan_deepfake (accessed on 4 January 2023)2022
Wang et al. [75]Multi-modal Multi-scale Transformer (M2TR)FaceForensics++ [90]Acc = 97.93%https://github.com/wangjk666/M2TR-Multi-modal-Multi-scale-Transformers-for-Deepfake-Detection (accessed on 4 January 2023)2022
Reenactment Generation
Zhang et al. [145]Decoder + warping CelebA-HQ [146]
FFHQ [147]
RAF-DB [148]		AU = 75.1%
AU = 70.9%
AU = 71.1%		https://github.com/bj80heyue/One_Shot_Face_Reenactment (accessed on 4 January 2023)2019
Ngo et al. [149]Encoder-decoder300VW [150]CL= 1.46×2020
Tripathy et al. [151]Facial attribute controllable GANsFaceForensics++ [90]CSIM = 0.747×2021
Bounareli et al. [152]3D shape modelVoxCeleb [153]FID = 0.66×2022
Agarwal et al. [154]Audio-Visual Face Reenactment GANVoxCeleb [153]FID = 9.05https://github.com/mdv3101/AVFR-Gan/ (accessed on 4 January 2023)2023
Reenactment Detection
Nguyen et al. [155]AutoencoderFaceForensics++ [90]EER = 7.07%https://github.com/nii-yamagishilab/ClassNSeg (accessed on 4 January 2023)2019
Dang et al. [156]CNNs + Attention mechanism FaceForensics++ [90]AUC = 99.4%
EER = 3.4%		https://github.com/Jstehouwer/FFD_CVPR2020 (accessed on 4 January 2023)2020
Kim et al. [157]Knowledge DistillationFaceForensics++ [90]Acc = 86.97%×2021
Yu et al. [158]U-Net Structure FaceForensics++ [90]Acc = 97.26%×2022
Wu et al. [159]Multistream Vision Transformer NetworkFaceForensics++ [90]Acc = 94.46%×2022
Attribute Manipulation Generation
Lample et al. [160]Encoder-decoderCelebA [135]RMSE = 0.0009https://github.com/facebookresearch/FaderNetworks (accessed on 4 January 2023)2018
Liu et al. [161]Selective transfer GANsCelebA [135]Acc = 70.80%https://github.com/csmliu/STGAN (accessed on 4 January 2023)2019
Kim et al. [162]Real-time style map GANsCelebA-HQ [146]
AFHQ [163] 		FID = 4.03
FID = 6.71		https://github.com/naver-ai/StyleMapGAN (accessed on 4 January 2023)2021
Huang et al. [164]Multi-head encoder and decoderCelebA-HQ [146]
StyleMapGAN [162]		MSE = 0.023
FID = 7.550		×2022
Sun et al. [165]3D-aware generator with two decoupled latent codesFFHQ [147]FID = 28.2https://github.com/MrTornado24/FENeRF (accessed on 4 January 2023)2022
Attribute Manipulation Detection
Wang et al. [166]CNNsOwn datasetAcc = 90.0%https://github.com/peterwang512/FALdetector (accessed on 4 January 2023)2019
Du et al. [167]DFT + CNNsDeepfake-in-the-wild [168]
Celeb-DF [142]
DFDC [3]		Acc = 78.00%
Acc = 96.00%
Acc = 81.00%		×2020
Akhtar et al. [36]DNNs Own datasetAcc = 99.31×2021
Rathgeb et al. [169]Human majority votingFERET [170]CCR = 62.8%×2022
Guo et al. [171]Gradient operator convolutional network with tensor pre-processing and manipulation trace attention moduleFaceForensics++ [90]Acc = 94.86%https://github.com/EricGzq/GocNet-pytorch (accessed on 4 January 2023)2023
Entire face synthesis generation
Li et al. [172]Conditional self-attention GANsCelebA-HQ [146]KID = 0.62https://github.com/LiYuhangUSTC/Lines2Face (accessed on 4 January 2023)2019
Karras et al. [81]StyleGANFFHQ [147]FID = 3.31https://github.com/NVlabs/stylegan2 (accessed on 4 January 2023)2020
Xia et al. [173]Textual descriptions GANsCelebA-HQ [146]FID = 106.37https://github.com/IIGROUP/TediGAN (accessed on 4 January 2023)2021
Song et al. [174]Text-to-speech systemLibriTTS dataset [175]
AISHELL-3 [176]		FPS = 30.3×2022
Li et al. [177]StyleT2I: High-Fidelity Text-to-Image SynthesisCelebA-HQ [146]FID = 18.02https://github.com/zhihengli-UR/StyleT2I (accessed on 4 January 2023)2022
Entire face synthesis detection
Wang et al. [178]CNNsStyleGAN2 [81]
ProGAN [146]		AP = 99.10%
AP = 100%		https://github.com/peterwang512/CNNDetection (accessed on 4 January 2023)2020
Pu et al. [179]Incremental clusteringPGGAN [146]F1 Score = 99.09%https://github.com/jmpu/NoiseScope (accessed on 4 January 2023)2020
Yousaf et al. [180]Two-Stream CNNsStarGAN
[101]		Acc = 96.32%×2021
Nowroozi et al. [181]Cross-band and spatial co-occurrence matrix + CNNsStyleGAN2 [81]
VIPPrint [182]		Acc = 93.80%
Acc = 92.56%		×2022
Boyd et al. [183]Human-annotated saliency maps into a deep learning loss functionStyleGAN2 [81], ProGAN [146], StyleGAN [147], StyleGAN2-ADA [184], StyleGAN3 [185], StarGANv2 [163],
SREFI [186]		AUC = 0.633https://github.com/BoydAidan/CYBORG-Loss (accessed on 4 January 2023)2023
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