CoChat
Sign Up
Cited by 40Generative Adversarial Networks and Image SynthesisDigital Media Forensic DetectionAdvanced Neural Network Applications

Generative AI in depth: A survey of recent advances, model variants, and real-world applications

Open access
Yazdani, Shamim, Singh, Akansha, Saxena, Nripsuta, Wang, Zichong, Palikhe, Avash, Pan, Deng, Pal, Umapada, Yang, Jie +1 more·2025·Journal of Big Data
DOI 10.1186/s40537-025-01247-x
Open this paper in CoChat — free accountOpen PDF

Abstract

In recent years, deep learning based generative models, particularly Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models (DMs), have been instrumental in generating diverse, high-quality content across various domains, such as image and video synthesis. This capability has led to widespread adoption of these models and has captured strong public interest. As they continue to advance at a rapid pace, the growing volume of research, expanding application areas, and unresolved technical challenges make it increasingly difficult to stay current. To address this need, this survey introduces a comprehensive taxonomy that organizes the literature and provides a cohesive framework for understanding the development of GANs, VAEs, and DMs, including their many variants and combined approaches. We highlight key innovations that have improved the quality, diversity, and controllability of generated outputs, reflecting the expanding potential of generative artificial intelligence. In addition to summarizing technical progress, we examine rising ethical concerns, including the risks of misuse and the broader societal impact of synthetic media. Finally, we outline persistent challenges and propose future research directions, offering a structured and forward looking perspective for researchers in this fast evolving field.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s40537-025-01247-x

Cites

14 of 14 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Generative AI systems can produce high-quality synthetic audiovisual content near-indistinguishable from authentic content.

    “Generative AI systems are now capable of producing high-quality synthetic audiovisual content (e.g., images, video, audio, text) that is near-indistinguishable from authentic content (Yazdani et al. 2025).”

    From Deepfakes, Democracy, and the Ethics of Synthetic Media: A Synthesis of the SOLARIS Project (2026) · cites this paper as Yazdani et al. 2025

    The Introduction says GANs produce remarkably realistic image and video samples, and “Deepfakes and misuse” describes convincing synthetic images and videos, voice cloning, and difficulty verifying authenticity. “Content creation” also discusses generated text. The paper does not establish that generated audio and text are near-indistinguishable from authentic content, so the claim extends its realism assessment across more formats than the passages support. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 119

  1. Gozalo-Brizuela R, Garrido-Merchán EC. A survey of generative ai applications. arXiv preprint; 2023. arXiv: 2306. 02781
  2. Jin H, Wei W, Wang X, Zhang W, Wu Y. Rethinking learning rate tuning in the era of large language models; 2023. arXiv preprint arXiv: 2309. 08859
  3. Amon A, Yin Z, Wang Z, Avash P, Zhang W. Uncertain boundaries: multidisciplinary approaches to copyright issues in generative AI; 2024. arXiv preprint arXiv: 2404. 08221
  4. Elgammal A, Liu B, Elhoseiny M, Mazzone M. Can: creative adversarial networks, generating" art" by learning about styles and deviating from style norms; 2017. arXiv preprint arXiv: 1706. 07068
  5. Li Y, Fang C, Yang J, Wang Z, Lu X, Yang M-H. Universal style transfer via feature transforms. Advances in neural information processing systems; 2017.
  6. Ehtesham A, Kumar S, Singh A, Khoei TT. Movie gen: Swot analysis of meta's generative AI foundation model for transforming media generation, advertising, and entertainment industries. In: 2025 IEEE 15th annual computing and communication workshop and conference (CCWC); 2025. p. 00189-95 . https:// doi. org/ 10. 1109/ CCWC6 2904. 2025. 10903 780 · DOI
  7. Gatys LA, Ecker AS, Bethge M. Image style transfer using convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. p. 2414-23.
  8. Pinaya WH, Graham MS, Kerfoot E, Tudosiu P-D, Dafflon J, Fernandez V, Sanchez P, Wolleb J, Costa PF, Patel A, et al. Generative AI for medical imaging: extending the monai framework; 2023. arXiv preprint arXiv: 2307. 15208
  9. Jing B, Erives E, Pao-Huang P, Corso G, Berger B, Jaakkola T. Eigenfold: generative protein structure prediction with diffusion models; 2023. arXiv preprint arXiv: 2304. 02198
  10. Shokrollahi Y, Yarmohammadtoosky S, Nikahd MM, Dong P, Li X, Gu L. A comprehensive review of generative ai in healthcare; 2023. arXiv preprint arXiv: 2310. 00795
  11. Ktena I, Wiles O, Albuquerque I, Rebuffi S-A, Tanno R, Roy AG, Azizi S, Belgrave D, Kohli P, Karthikesalingam A, et al. Generative models improve fairness of medical classifiers under distribution shifts; 2023. arXiv preprint arXiv: 2304. 09218
  12. Perez L, Wang J. The effectiveness of data augmentation in image classification using deep learning; 2017. arXiv preprint arXiv: 1712. 04621
  13. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial nets. Advances in neural information processing systems; 2014.
  14. Kingma DP, Welling M. Auto-encoding variational Bayes; 2013. arXiv preprint arXiv: 1312. 6114
  15. Rezende DJ, Mohamed S, Wierstra D. Stochastic backpropagation and approximate inference in deep generative models. In: International conference on machine learning; 2014. p. 1278-86. PMLR
  16. Karras T, Aittala M, Lehtinen J, Hellsten J, Aila T, Laine S. Analyzing and improving the training dynamics of diffusion models; 2023. arXiv preprint arXiv: 2312. 02696
  17. Wu Z, Zhou P, Kawaguchi K, Zhang H. Fast diffusion model; 2023. arXiv preprint arXiv: 2306. 06991
  18. Yang R, Srivastava P, Mandt S. Diffusion probabilistic modeling for video generation. Entropy. 2023;25(10):1469.
  19. Croitoru F-A, Hondru V, Ionescu RT, Shah M. Diffusion models in vision: a survey. IEEE Trans Pattern Anal Mach Intell. 2023;45:10850-69.
  20. De Bortoli V, Thornton J, Heng J, Doucet A. Diffusion schrödinger bridge with applications to score-based genera- tive modeling. Adv Neural Inf Process Syst. 2021;34:17695-709.
  21. Bengesi S, El-Sayed H, Sarker MK, Houkpati Y, Irungu J, Oladunni T. Advancements in generative AI: a comprehen- sive review of gans, gpt, autoencoders, diffusion model, and transformers; 2023. arXiv preprint arXiv: 2311. 10242
  22. Ali S, DiPaola D, Breazeal C. What are gans?: introducing generative adversarial networks to middle school stu- dents. In: Proceedings of the AAAI conference on artificial intelligence, vol 35; 2021. p. 15472-79
  23. Isola P, Zhu J-Y, Zhou T, Efros AA. Image-to-image translation with conditional adversarial networks. In: Proceed- ings of the IEEE conference on computer vision and pattern recognition; 2017. p. 1125-34.
  24. Zhang Y, Bai Y, Ding M, Ghanem B. Multi-task generative adversarial network for detecting small objects in the wild. Int J Comput Vision. 2020;128:1810-28.
  25. Xu W, Long C, Wang R, Wang G. Drb-gan: a dynamic resblock generative adversarial network for artistic style transfer. In: Proceedings of the IEEE/CVF international conference on computer vision; 2021. p. 6383-92.
  26. Nishimura Y, Nakamura Y, Ishiguro H. Human interaction behavior modeling using generative adversarial net- works. Neural Netw. 2020;132:521-31.
  27. Gregor K, Danihelka I, Graves A, Rezende D, Wierstra D. Draw: a recurrent neural network for image generation. In: International conference on machine learning. PMLR; 2015. p. 1462-71.
  28. Yan W, Zhang Y, Abbeel P, Srinivas A. Videogpt: Video generation using VQ-VAE and transformers; 2021. arXiv preprint arXiv: 2104. 10157
  29. Cheng S, Guo Y, Arcucci R. A generative model for surrogates of spatial-temporal wildfire nowcasting. IEEE Trans Emerg Top Comput Intell. 2023;7:1420-30.
  30. Güngör A, Dar SU, Öztürk Ş, Korkmaz Y, Bedel HA, Elmas G, Ozbey M, Çukur T. Adaptive diffusion priors for acceler- ated MRI reconstruction. Med Image Anal. 2023;88: 102872.
  31. Arjovsky M, Chintala S, Bottou L. Wasserstein generative adversarial networks. In: International Conference on Machine Learning. PMLR; 2017. p. 214-23.
  32. Dosovitskiy A, Brox T. Generating images with perceptual similarity metrics based on deep networks. Advances in neural information processing systems, vol. 29; 2016.
  33. Larsen ABL, Sønderby SK, Larochelle H, Winther O. Autoencoding beyond pixels using a learned similarity metric. In: International Conference on Machine Learning. PMLR; 2016. p. 1558-66.
  34. Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adver- sarial networks; 2015. arXiv preprint arXiv: 1511. 06434
  35. Zhao S, Song J, Ermon S. Infovae: Information maximizing variational autoencoders; 2017. arXiv preprint arXiv: 1706. 02262
  36. Cao H, Wang J, Ren T, Qi X, Chen Y, Yao Y, Zhang L. Exploring vision transformers as diffusion learners; 2022. arXiv preprint arXiv: 2212. 13771
  37. Cordts M, Omran M, Ramos S, Rehfeld T, Enzweiler M, Benenson R, Franke U, Roth S, Schiele B. The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. p. 3213-23.
  38. Tyleček R, Šára R. Spatial pattern templates for recognition of objects with regular structure. In: Pattern recogni- tion: 35th German conference, GCPR 2013, Saarbrücken, Germany, September 3-6, 2013. Proceedings 35, 2013. Springer; 2013. p. 364-74.
  39. Xie S, Tu Z. Holistically-nested edge detection. In: Proceedings of the IEEE International Conference on Computer Vision, 2015; 1395-403.
  40. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, et al. Ima- genet large scale visual recognition challenge. Int J Comput Vision. 2015;115:211-52.
  41. Yu A, Grauman K. Fine-grained visual comparisons with local learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2014. p. 192-9.
  42. Zhu J-Y, Krähenbühl P, Shechtman E, Efros AA. Generative visual manipulation on the natural image manifold. In: Computer vision-ECCV 2016: 14th European conference, Amsterdam, The Netherlands, October 11-14, 2016, proceedings, Part V 14. Springer; 2016. p. 597-613.
  43. Eitz M, Hays J, Alexa M. How do humans sketch objects? ACM Trans Graph TOG. 2012;31(4):1-10.
  44. Laffont P-Y, Ren Z, Tao X, Qian C, Hays J. Transient attributes for high-level understanding and editing of outdoor scenes. ACM Trans Graph TOG. 2014;33(4):1-11.
  45. Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick CL. Microsoft coco: Common objects in context. In: Computer vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, proceedings, Part V 13. Springer; 2014. p. 740-55.
  46. Yang S, Luo P, Loy C-C, Tang X. Wider face: a face detection benchmark. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. p. 5525-33.
  47. Avrahami O, Lischinski D, Fried O. Blended diffusion for text-driven editing of natural images. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition; 2022. p. 18208-18.
  48. Nichol A, Dhariwal P, Ramesh A, Shyam P, Mishkin P, McGrew B, Sutskever I, Chen M. Glide: towards photorealistic image generation and editing with text-guided diffusion models; 2021. arXiv preprint arXiv: 2112. 10741
  49. Harvey W, Naderiparizi S, Masrani V, Weilbach C, Wood F. Flexible diffusion modeling of long videos. Adv Neural Inf Process Syst. 2022;35:27953-65.
  50. Ho J, Salimans T, Gritsenko AA, Chan W, Norouzi M, Fleet DJ. Video diffusion models. In: Proceedings of DGM4HSD; 2022
  51. Zhou L, Du Y, Wu J. 3d shape generation and completion through point-voxel diffusion. In: Proceedings of the IEEE/CVF international conference on computer vision; 2021. p. 5826-35.
  52. Huang N, Tang F, Dong W, Xu C. Draw your art dream: Diverse digital art synthesis with multimodal guided diffu- sion. In: Proceedings of the 30th ACM international conference on multimedia; 2022. p. 1085-94.
  53. Thomee B, Shamma DA, Friedland G, Elizalde B, Ni K, Poland D, Borth D, Li L-J. Yfcc100m: the new data in multime- dia research. Commun ACM. 2016;59(2):64-73.
  54. Huang N, Zhang Y, Tang F, Ma C, Huang H, Zhang Y, Dong W, Xu C. Diffstyler: controllable dual diffusion for text- driven image stylization; 2022. arXiv preprint arXiv: 2211. 10682
  55. Changpinyo S, Sharma P, Ding N, Soricut R. Conceptual 12m: pushing web-scale image-text pre-training to recognize long-tail visual concepts. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition; 2021. p. 3558-68.
  56. Netzer Y, Wang T, Coates A, Bissacco A, Wu B, Ng AY. Reading digits in natural images with unsupervised feature learning. NIPS workshop on deep learning and unsupervised feature learning; 2011. http:// ufldl. stanf ord. edu/ house numbe rs/
  57. Zhou B, Lapedriza A, Xiao J, Torralba A, Oliva A. Learning deep features for scene recognition using places data- base. Advances in neural information processing systems; 2014. p. 27.
  58. Kumar A, Irsoy O, Ondruska P, Iyyer M, Bradbury J, Gulrajani I, Zhong V, Paulus R, Socher R. Ask me anything: dynamic memory networks for natural language processing. In: International conference on machine learning. PMLR; 2016. p. 1378-87.
  59. OpenAI R. Gpt-4 technical report. arxiv 2303.08774. View in Article 2; 2023. p. 3. · arXiv
  60. Johnson AE, Pollard TJ, Berkowitz SJ, Greenbaum NR, Lungren MP, Deng C-Y, Mark RG, Horng S. Mimic-cxr, a de- identified publicly available database of chest radiographs with free-text reports. Sci Data. 2019;6(1):317.
  61. Song J, Meng C, Ermon S. Denoising diffusion implicit models; 2020. arXiv preprint arXiv: 2010. 02502
  62. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, Downey P, Elliott P, Green J, Landray M, et al. Uk biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12(3):1001779.
  63. Knoll F, Zbontar J, Sriram A, Muckley MJ, Bruno M, Defazio A, Parente M, Geras KJ, Katsnelson J, Chandarana H, et al. FASTMRI: a publicly available raw k-space and DICOM dataset of knee images for accelerated MR image recon- struction using machine learning. Radiol Artif Intell. 2020;2(1): 190007.
  64. Chakravarty D, Porter LL. Alphafold2 fails to predict protein fold switching. Protein Sci. 2022;31(6):4353.
  65. Antoniou A, Storkey A, Edwards H. Data augmentation generative adversarial networks; 2017. arXiv preprint arXiv: 1711. 04340
  66. Cohen G, Afshar S, Tapson J, Schaik A. Emnist: extending mnist to handwritten letters; 2017. arXiv preprint arXiv: 1702. 05373. https:// www. nist. gov/ itl/ produ cts-and-servi ces/ emnist-datas et 79. Parkhi OM, Vedaldi A, Zisserman A. Deep face recognition. British machine vision conference; 2015. http:// www. robots. ox. ac. uk/ ~vgg/ data/ vgg_ face/
  67. Giglio L, Schroeder W, Justice CO. The collection 6 modis active fire detection algorithm and fire products. Remote Sens Environ. 2016;178:31-41.
  68. Schroeder W, Oliva P, Giglio L, Csiszar IA. The new viirs 375 m active fire detection data product: algorithm descrip- tion and initial assessment. Remote Sens Environ. 2014;143:85-96.
  69. Katara P, Xian Z, Fragkiadaki K. Gen2sim: scaling up robot learning in simulation with generative models; 2023. arXiv preprint arXiv: 2310. 18308
  70. Geng H, Xu H, Zhao C, Xu C, Yi L, Huang S, Wang H. Gapartnet: Cross-category domain-generalizable object perception and manipulation via generalizable and actionable parts. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition; 2023. p. 7081-91.
  71. Dong Y, Liu Y, Zhang H, Chen S, Qiao Y. Fd-gan: Generative adversarial networks with fusion-discriminator for sin- gle image dehazing. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34; 2020. p. 10729-36.
  72. Li B, Ren W, Fu D, Tao D, Feng D, Zeng W, Wang Z. Benchmarking single-image dehazing and beyond. IEEE Trans Image Process. 2018;28(1):492-505.
  73. Ancuti C, Ancuti CO, Timofte R. Ntire 2018 challenge on image dehazing: Methods and results. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops; 2018. p. 891-901.
  74. Yi W, Dong L, Liu M, Hui M, Kong L, Zhao Y. Sid-net: single image dehazing network using adversarial and contras- tive learning. Multimedia Tools Appl. 2024;83(28):71619-38.
  75. Ancuti C, Ancuti CO, Timofte R, De Vleeschouwer C. I-haze: A dehazing benchmark with real hazy and haze-free indoor images. In: Advanced concepts for intelligent vision systems: 19th international conference, ACIVS 2018, Poitiers, France, September 24-27, 2018, Proceedings 19,2018; Springer. p. 620-31.
  76. Choi LK, You J, Bovik AC. Referenceless prediction of perceptual fog density and perceptual image defogging. IEEE Trans Image Process. 2015;24(11):3888-901.
  77. Yinka-Banjo C, Ugot O-A. A review of generative adversarial networks and its application in cybersecurity. Artif Intell Rev. 2020. https:// doi. org/ 10. 1007/ s10462-019-09717-4. · DOI
  78. Ho J, Jain A, Abbeel P. Denoising diffusion probabilistic models. Adv Neural Inf Process Syst. 2020;33:6840-51.
  79. Bond-Taylor S, Leach A, Long Y, Willcocks CG. Deep generative modelling: a comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models. IEEE Trans Pattern Anal Mach Intell. 2021;44:7327-47.
  80. Mao X, Li Q, Xie H, Lau RY, Wang Z, Paul Smolley S. Least squares generative adversarial networks. In: Proceedings of the IEEE international conference on computer vision; 2017. p. 2794-802.
  81. Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville AC. Improved training of wasserstein gans. Advances in neural information processing systems; 2017.
  82. Miyato T, Kataoka T, Koyama M, Yoshida Y. Spectral normalization for generative adversarial networks; 2018. arXiv preprint arXiv: 1802. 05957
  83. Dumoulin V, Belghazi I, Poole B, Mastropietro O, Lamb A, Arjovsky M, Courville A. Adversarially learned inference; 2016. arXiv preprint arXiv: 1606. 00704
  84. Goodfellow I. Nips 2016 tutorial: Generative adversarial networks; 2016. arXiv preprint arXiv: 1701. 00160.
  85. Arora S, Ge R, Liang Y, Ma T, Zhang Y. Generalization and equilibrium in generative adversarial nets (gans). In: International conference on machine learning. PMLR; 2017. p. 224-32. 100. Sajeeda A, Hossain BM. Exploring generative adversarial networks and adversarial training. Int J Cogn Comput Eng. 2022;3:78-89. 101. Ratliff LJ, Burden SA, Sastry SS. Characterization and computation of local nash equilibria in continuous games. In: 2013 51st annual Allerton conference on communication, control, and computing (Allerton). IEEE; 2013. p. 917-24. 102. Jabbar A, Li X, Omar B. A survey on generative adversarial networks: variants, applications, and training. ACM Comput Surv CSUR. 2021;54(8):1-49. 103. Salimans T, Goodfellow I, Zaremba W, Cheung V, Radford A, Chen X. Improved techniques for training GANS. Advances in neural information processing systems; 2016. 104. Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International conference on machine learning. PMLR; 2015. p. 448-56. 105. Denton EL, Chintala S, Fergus R, et al. Deep generative image models using a Laplacian pyramid of adversarial networks. Advances in neural information processing systems; 2015. 106.
  86. Mirza M, Osindero S. Conditional generative adversarial nets; 2014. arXiv preprint arXiv: 1411. 1784 107.
  87. Kumar JK. School of engineering and architecture. PhD thesis, ALMA MATER STUDIORUM-UNIVERSITÀ DI BOLO- GNA; 2022 108.
  88. Hoffman MD, Blei DM, Wang C, Paisley J. Stochastic variational inference. J Mach Learn Res. 2013;14:1303-47. 109. Gretton A, Borgwardt K, Rasch MJ, Scholkopf B, Smola AJ. A kernel method for the two-sample problem; 2008. arXiv preprint arXiv: 0805. 2368 110.
  89. Li Y, Swersky K, Zemel R. Generative moment matching networks. In: International conference on machine learn- ing. PMLR; 2015. p. 1718-27. 111. Dziugaite GK, Roy DM, Ghahramani Z. Training generative neural networks via maximum mean discrepancy optimization; 2015. arXiv preprint arXiv: 1505. 03906 112.
  90. Maaløe L, Sønderby CK, Sønderby SK, Winther O. Auxiliary deep generative models. In: International conference on machine learning. PMLR; 2016. p. 1445-53. 113. Xu M, Yu L, Song Y, Shi C, Ermon S, Tang J. Geodiff: A geometric diffusion model for molecular conformation gen- eration; 2022. arXiv preprint arXiv: 2203. 02923 114.
  91. Yang L, Huang Z, Zhang Z, Liu Z, Hong S, Zhang W, Yang W, Cui B, Zhang L. Graphusion: latent diffusion for graph generation. IEEE Trans Knowl Data Eng. 2024;36:6358-69. 115.
  92. Sohl-Dickstein J, Weiss E, Maheswaranathan N, Ganguli S. Deep unsupervised learning using nonequilibrium thermodynamics. In: International conference on machine learning. PMLR; 2015. p. 2256-65. 116.
  93. Song Y, Sohl-Dickstein J, Kingma DP, Kumar A, Ermon S, Poole B. Score-based generative modeling through sto- chastic differential equations; 2020. arXiv preprint arXiv: 2011. 13456 117.
  94. Chang Z, Koulieris GA, Shum HP. On the design fundamentals of diffusion models: a survey; 2023. arXiv preprint arXiv: 2306. 04542 122.
  95. Popov V, Vovk I, Gogoryan V, Sadekova T, Kudinov M. Grad-tts: A diffusion probabilistic model for text-to-speech. In: International conference on machine learning. PMLR; 2021. p. 8599-608. 123. Bao F, Li C, Cao Y, Zhu J. All are worth words: a vit backbone for score-based diffusion models; 2022. arXiv preprint arXiv: 2209. 12152 124.
  96. Song Y, Ermon S. Improved techniques for training score-based generative models. Adv Neural Inf Process Syst. 2020;33:12438-48. 129.
  97. Song Y, Ermon, S. Generative modeling by estimating gradients of the data distribution. Advances in neural infor- mation processing systems; 2019. 130. Jolicoeur-Martineau A, Piché-Taillefer R, Combes RTD, Mitliagkas I. Adversarial score matching and improved sampling for image generation; 2020. arXiv preprint arXiv: 2009. 05475 131.
  98. Chen X, Duan Y, Houthooft R, Schulman J, Sutskever I, Abbeel P. Infogan: Interpretable representation learning by information maximizing generative adversarial nets. Advances in neural information processing systems; 2016. 132. Ye F, Bors AG. Self-supervised adversarial variational learning. Pattern Recogn. 2023;148: 110156. 133. Gur S, Benaim S, Wolf L. Hierarchical patch VAE-GAN: generating diverse videos from a single sample. Adv Neural Inf Process Syst. 2020;33:16761-72. 134.
  99. Chinta SV, Wang Z, Yin Z, Hoang N, Gonzalez M, Quy TL, Zhang W. Fairaied: navigating fairness, bias, and ethics in educational ai applications; 2024. arXiv preprint arXiv: 2407. 18745 135.
  100. Chinta SV, Wang Z, Palikhe A, Zhang X, Kashif A, Smith MA, Liu J, Zhang W. Ai-driven healthcare: a survey on ensur- ing fairness and mitigating bias. PLoS Digital Health. 2025;4(5):0000864. 136. Alzubaidi L, Bai J, Al-Sabaawi A, Santamaría J, Albahri A, Al-dabbagh BSN, Fadhel MA, Manoufali M, Zhang J, Al- Timemy AH, et al. A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications. J Big Data. 2023;10(1):46. 137.
  101. Raner V, Joshi A, Sawant S. Medical image synthesis using generative adversarial networks. GANs Data Augment Healthc. 2023. https:// doi. org/ 10. 1007/ 978-3-031-43205-7_3. 138. Park T, Liu M-Y, Wang T-C, Zhu J-Y. Semantic image synthesis with spatially-adaptive normalization. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition; 2019. p. 2337-46. 139. Lin Z, Shi Y, Xue Z. Idsgan: Generative adversarial networks for attack generation against intrusion detection. In: Pacific-Asia conference on knowledge discovery and data mining. Springer; 2022. p. 79-91. 140. Zhang C, Zhang C, Zhang M, Kweon IS. Text-to-image diffusion model in generative AI: a survey; 2023. arXiv preprint arXiv: 2303. 07909 141. · DOI
  102. Lembono TS, Pignat E, Jankowski J, Calinon S. Learning constrained distributions of robot configurations with generative adversarial network. IEEE Robot Autom Lett. 2021;6(2):4233-40. 142. Zhu J-Y, Park T, Isola P, Efros AA. Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision; 2017. p. 2223-32. 143. Perry GL, Wainwright J, Etherington TR, Wilmshurst JM. Experimental simulation: using generative modeling and palaeoecological data to understand human-environment interactions. Front Ecol Evol. 2016;4:109. 144. Liang D, Zhang Z, Zhu XX. Deep learning for remote sensing image understanding: a survey. Remote Sens Envi- ron. 2020;240:111749. 145. Reed S, Akata Z, Lee H, Schiele B. Learning deep representations of fine-grained visual descriptions. In: Proceed- ings of the IEEE conference on computer vision and pattern recognition; 2016. p. 49-58. 146. Landes WM, Posner RA. The economic structure of intellectual property law. Harvard University Press; 2003. 147. Xu E, Zhang W, Xu W. Transforming digital forensics with large language models: Unlocking automation, insights, and justice. In: Proceedings of the 33rd ACM international conference on information and knowledge manage- ment; 2024. p. 5543-6. 148. Yin Z, Wang Z, Xu W, Zhuang J, Mozumder P, Smith A, Zhang W. Digital forensics in the age of large language models; 2025. arXiv preprint arXiv: 2504. 02963 149.
  103. Franceschelli G, Musolesi M. Copyright in generative deep learning. Data & Policy. 2022;4:17. 150. Sag M. Copyright safety for generative ai. Forthcoming in the Houston Law Review; 2023 151. Poland CM. Generative ai and us intellectual property law; 2023. arXiv preprint arXiv: 2311. 16023 152.
  104. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. Uk biobank: an open access resource for identify- ing the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12(3):1001779. 157. Feng Y, Shah C. Has ceo gender bias really been fixed? adversarial attacking and improving gender fairness in image search. In: Proceedings of the AAAI conference on artificial intelligence, vol. 36; 2022. p. 11882-90. 158. Yao R, Cui Z, Li X, Gu L. Improving fairness in image classification via sketching; 2022. arXiv preprint arXiv: 2211. 00168 159.
  105. Liu Z, Luo P, Wang X, Tang X. Deep learning face attributes in the wild. In: Proceedings of international conference on computer vision (ICCV); 2015 160.
  106. Codella N, Rotemberg V, Tschandl P, Celebi ME, Dusza S, Gutman D, Helba B, Kalloo A, Liopyris K, Marchetti M, Kit- tler H, Halpern A. Skin lesion analysis toward melanoma detection 2018: a challenge hosted by the international skin imaging collaboration (ISIC); 2018. 161. Hwang S, Park S, Kim D, Do M, Byun H. Fairfacegan: Fairness-aware facial image-to-image translation; 2020. arXiv preprint arXiv: 2012. 00282 162.
  107. Booth BM, Hickman L, Subburaj SK, Tay L, Woo SE, D'Mello SK. Bias and fairness in multimodal machine learning: a case study of automated video interviews. In: Proceedings of the 2021 international conference on multimodal interaction; 2021. p. 268-77. 163.
  108. Kim C, Choi J, Yoon J, Yoo D, Lee W. Fairness-aware multimodal learning in automatic video interview assessment. IEEE Access. 2023;11:122677-93. 164. Bojchevski A, Shchur O, Zügner D, Günnemann S. Netgan: generating graphs via random walks. In: International conference on machine learning. PMLR; 2018. p. 610-19. 165. Zhou D, Zheng L, Han J, He J. A data-driven graph generative model for temporal interaction networks. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining; 2020. p. 401-11. 166. Wang Z, Wallace C, Bifet A, Yao X, Zhang W. Fairness-aware graph generative adversarial networks. In: Joint Euro- pean conference on machine learning and knowledge discovery in databases. Springer; 2023. p. 259-75. 167. Wang Z, Zhang W. Fdgen: A fairness-aware graph generation model. In: Proceedings of the 42nd international conference on machine learning. PMLR; 2025. 168. Wang Z, Yin Z, Zhang Y, Yang L, Zhang T, Pissinou N, Cai Y, Hu S, Li Y, Zhao L, et al. FG-smote: towards fair node clas- sification with graph neural network. ACM SIGKDD Explorations Newsl. 2025;26(2):99-108. 169. Wang Z, Saxena N, Yu T, Karki S, Zetty T, Haque I, Zhou S, Kc D, Stockwell I, Bifet A, et al. Preventing discriminatory decision-making in evolving data streams. In: Proceedings of the 2023 ACM conference on fairness, accountability, and transparency (FAccT); 2023 170.
  109. George AS, George AH. Deepfakes: the evolution of hyper realistic media manipulation. Partners Univ Innov Res Publ. 2023;1(2):58-74. 171. Marr B. What is Generative AI: a super-simple explanation anyone can understand. Forbes Magazine; 2023. https:// www. forbes. com/ sites/ berna rdmarr/ 2023/ 09/ 19/ what-is-gener ative-ai-a-super-simple-expla nation-anyone-can-/ under stand/ 172.
  110. Choi CQ. AI creates fake Obama. IEEE Spectrum 2022. https:// spect rum. ieee. org/ ai-creat es-fake-obama 176.
  111. Rossler A, Cozzolino D, Verdoliva L, Riess C, Thies J, Nießner M. Faceforensics++: learning to detect manipulated facial images. In: Proceedings of the IEEE/CVF international conference on computer vision; 2019. p. 1-11 178.
  112. Saxena D, Cao J. Generative adversarial networks (GANS): challenges. Solut Future Direct. 2020;2005:00065. 179. Kim T, Cha M, Kim H, Lee JK, Kim J. Learning to discover cross-domain relations with generative adversarial net- works. In: International conference on machine learning. PMLR; 2017. p. 1857-65 . PMLR 180.
  113. Bau D, Zhu J-Y, Strobelt H, Zhou B, Tenenbaum JB, Freeman WT, Torralba A. Gan dissection: Visualizing and under- standing generative adversarial networks; 2018. arXiv preprint arXiv: 1811. 10597 181.
  114. Rebol M, Gütl C, Pietroszek K. Real-time gesture animation generation from speech for virtual human interaction. In: Extended abstracts of the 2021 CHI conference on human factors in computing systems; 2021. p. 1-4. 183. Hendrycks D, Gimpel K. A baseline for detecting misclassified and out-of-distribution examples in neural net- works; 2016. arXiv preprint arXiv: 1610. 02136 184.
  115. Hacker P. Ai regulation in Europe: from the AI act to future regulatory challenges; 2023. arXiv preprint arXiv: 2310. 04072 185.
  116. Bacalu F, et al. Biometric facial recognition technology, law enforcement algorithmic automation, and data-driven predictive policing systems in human rights protections and abuses. Rev Contemp Philos. 2022;21:38-54. 186. Milossi M. Remote biometric identification systems and ethical challenges: The case of facial recognition. In: 2021 6th South-East Europe design automation, computer engineering, computer networks and social media confer- ence (SEEDA-CECNSM). IEEE; 2021. p. 1-6. 187. The United States Government; 2023. https:// www. white house. gov/ briefi ng-room/ state ments-relea ses/ 2023/ 10/ 30/ fact-sheet-presi dent-biden-issues-execu tive-order-on-safe-secure-and-/ trust worthy-artifi cial-intel ligen ce/ 188. https:// www. congr ess. gov/ bill/ 117th-congr ess/ house-bill/ 9631?s= 1&r=1 189.
  117. Hinchey M. Hinchey Bill to ban non-consensual deepfake images signed into law; 2023. https:// www. nysen ate. gov/ newsr oom/ press-relea ses/ 2023/ miche lle-hinch ey/ hinch ey-bill-ban-non-conse nsual-deepf ake-images 190. https:// c2pa. org/ 191. https:// leica-camera. com/ en-US/ photo graphy/ conte nt-crede ntials 192.
  118. Chu Z, Wang Z, Zhang W. Fairness in large language models: a taxonomic survey. ACM SIGKDD Explorations Newsl. 2024;2024:34-48. 195. Li Z, Wu X, Du H, Nghiem H, Shi G. Benchmark evaluations, applications, and challenges of large vision language models: a survey. arXiv preprint arXiv: 2501. 02189 196.
  119. Wang Z, Palikhe A, Zhang W. Fairness definitions in language models explained; 2024. arXiv preprint arXiv: 2407. 18454

Cited by

Sorted by citations

Authors
Yazdani, S., Singh, A., Saxena, N., Wang, Z., Palikhe, A., Pan, D., Pal, U., Yang, J., & Zhang, W.
Affiliations
  • Florida International University (US)
  • Manipal Academy of Higher Education (IN)
  • University of Southern California (US)
  • Indian Statistical Institute (IN)
Corresponding author
Zhang, Wenbin
ORCID
Venue
Journal of Big Data
Publisher
Springer Science and Business Media LLC
Published
2025-10-08
Volume
12
Issue
1
ISSN
2196-1115 (ISSN-L)
DOI
10.1186/s40537-025-01247-x
OpenAlex
W4414930662
Cited by
40
References
14
Citations by year
2026: 38 · 2025: 2
FWCI
14.04
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal Of Big Data (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords
generative AI, generative adversarial networks, variational autoencoder, diffusion models, image generation, video generation, synthetic media, controllable generation, ethical risks of AI

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review