DE-FAKE: Detection and Attribution of Fake Images Generated by Text-to-Image Generation Models
Open accessAbstract
Text-to-image generation models that generate images based on prompt descriptions have attracted an increasing amount of attention during the past few months. Despite their encouraging performance, these models raise concerns about the misuse of their generated fake images. To tackle this problem, we pioneer a systematic study on the detection and attribution of fake images generated by text-to-image generation models. Concretely, we first build a machine learning classifier to detect the fake images generated by various text-to-image generation models. We then attribute these fake images to their source models, such that model owners can be held responsible for their models' misuse. We further investigate how prompts that generate fake images affect detection and attribution. We conduct extensive experiments on four popular text-to-image generation models, including DALL·E 2, Stable Diffusion, GLIDE, and Latent Diffusion, and two benchmark prompt-image datasets. Empirical results show that (1) fake images generated by various models can be distinguished from real ones, as there exists a common artifact shared by fake images from different models; (2) fake images can be effectively attributed to their source models, as different models leave unique fingerprints in their generated images; (3) prompts with the "person'' topic or a length between 25 and 75 enable models to generate fake images with higher authenticity. All findings contribute to the community's insight into the threats caused by text-to-image generation models. We appeal to the community's consideration of the counterpart solutions, like ours, against the rapidly-evolving fake image generation.
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Synthetic images aid the recognition of human-made art forgeries
Ostmeyer, J., Schaerf, L., Buividovich, P. V., Charles, T., Postma, E. O., & Popovici, C. (2024) · PLoS ONE · doi:10.1371/journal.pone.0295967 · Cited by 15 · see citing works
Katirai, A., García, N., Ide, K., Nakashima, Y., & Kishimoto, A. (2024) · AI and Ethics · doi:10.1007/s43681-024-00517-3 · Cited by 17 · see citing works
Exploring the Adversarial Robustness of CLIP for AI-generated Image Detection
Rosa, V. D., Guillaro, F., Poggi, G., Cozzolino, D., & Verdoliva, L. (2024) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs61860.2024.10810719 · Cited by 15 · see citing works
Advancements in AI-Generated Content Forensics: A Systematic Literature Review
Xu, Q., Mu, W., Li, J., Sun, T., & Jiang, X. (2025) · ACM Computing Surveys · doi:10.1145/3760526 · Cited by 16 · see citing works
Detecting AI-Generated Images Using a Hybrid ResNet-SE Attention Model
Gunukula, A. R., Gupta, H. D., & Sheng, V. S. (2025) · Applied Sciences · doi:10.3390/app15137421 · Cited by 13 · see citing works
Campos, D. S., de Oliveira, R. F., Vieira, L. D. O., Bragança, P. H. N., Nunes, J. L. S., Guimarães, E. C., & Ottoni, F. P. (2023) · Web Ecology · doi:10.5194/we-23-135-2023 · Cited by 13 · see citing works
SeqMIA: Sequential-Metric Based Membership Inference Attack
Li, H., Li, Z., Wu, S., Hu, C., Ye, Y., Zhang, M., Feng, D., & Zhang, Y. (2024) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3658644.3690335 · Cited by 13 · see citing works
Self-citationRethinking Open-World DeepFake Attribution with Multi-perspective Sensory Learning
Sun, Z., Chen, S., Yao, T., Yi, R., Ding, S., & Ma, L. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02184-7 · Cited by 13 · see citing works
Comanducci, L., Bestagini, P., & Tubaro, S. (2025) · Journal of Imaging · doi:10.3390/jimaging11070242 · Cited by 11 · see citing works
SIDBench: A Python framework for reliably assessing synthetic image detection methods
Schinas, M., & Papadopoulos, S. (2024) · ACM International Workshop on Multimedia AI against Disinformation (MAD) · doi:10.1145/3643491.3660277 · Cited by 10 · see citing works
Xu, Q., Jiang, X., Sun, T., Wang, H., Meng, L., & Yan, H. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102578 · Cited by 11 · see citing works
Hierarchical Progressive Image Forgery Detection and Localization Method Based on UNet
Liu, Y., Li, X., Zhang, J., Li, S., Hu, S., & Lei, J. W. (2024) · Big Data and Cognitive Computing · doi:10.3390/bdcc8090119 · Cited by 10 · see citing works
Whodunit: Detection and Attribution of Synthetic Images by Leveraging Model-specific Fingerprints
Wißmann, A., Zeiler, S., Nickel, R. M., & Kolossa, D. (2024) · ACM International Workshop on Multimedia AI against Disinformation (MAD) · doi:10.1145/3643491.3660280 · Cited by 9 · see citing works
Conditioned Prompt-Optimization for Continual Deepfake Detection
Laiti, F., Liberatori, B., Min, T. D., & Ricci, E. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78189-6_5 · Cited by 9 · see citing works
Song, J., Ye, D., & Zhang, Y. (2024) · IEEE Signal Processing Letters · doi:10.1109/lsp.2024.3522851 · Cited by 9 · see citing works
Self-citationSoK: On the Offensive Potential of AI
Schröer, S. L., Apruzzese, G., Human, S., Laskov, P., Anderson, H. S., Bernroider, E. W. N., Fass, A., Nassi, B., Rimmer, V., Roli, F., et al. (2025) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml64287.2025.00021 · Cited by 10 · see citing works
Robust CLIP-Based Detector for Exposing Diffusion Model-Generated Images
Santosh, Lin, L., Amerini, I., Wang, X., & Hu, S. (2024) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss61716.2024.10672612 · Cited by 9 · see citing works
LEDNet: a multimodal foundation model for robust deepfake detection
Tao, R., Tang, S., Qin, H., Wang, W., Wei, Y., & Zhao, Y. (2025) · Science China Information Sciences · doi:10.1007/s11432-024-4400-8 · Cited by 9 · see citing works
EvilPromptFuzzer: generating inappropriate content based on text-to-image models
He, J., Dai, H., Sui, R., Yuan, X., Liu, D., Feng, H., Liu, X., Yang, W., Cui, B., & Li, K. (2024) · Cybersecurity · doi:10.1186/s42400-024-00279-9 · Cited by 8 · see citing works
Are handcrafted filters helpful for attributing AI-generated images?
Li, J., Wang, H., Li, S., Qian, Z., Zhang, X., & Vasilakos, A. V. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680945 · Cited by 7 · see citing works
Which Model Generated This Image? A Model-Agnostic Approach for Origin Attribution
Liu, F., Luo, H., Li, Y., Torr, P. H. S., & Gu, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73033-7_16 · Cited by 7 · see citing works
Nguyen, T. D., Azizpour, A., & Stamm, M. C. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.00289 · Cited by 7 · see citing works
Secret Lies in Color: Enhancing AI-Generated Images Detection with Color Distribution Analysis
Jia, Z., Huang, C., Zhu, Y., Fei, H., Duan, X., Yuan, Z., Deng, Y., Zhang, J., Zhang, J., & Zhou, J. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.01255 · Cited by 7 · see citing works
Reliable Out-of-Distribution Recognition of Synthetic Images
Maier, A., & Rieß, C. (2024) · Journal of Imaging · doi:10.3390/jimaging10050110 · Cited by 7 · see citing works
DeepGuard: Identification and Attribution of AI-Generated Synthetic Images
Namani, Y., Reghioua, I., Bendiab, G., Labiod, M. A., & Shiaeles, S. N. (2025) · Electronics · doi:10.3390/electronics14040665 · Cited by 8 · see citing works
Not with My Name! Inferring Artists’ Names of Input Strings Employed by Diffusion Models
Leotta, R., Giudice, O., Guarnera, L., & Battiato, S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43148-7_31 · Cited by 7 · see citing works
D4: Detection of Adversarial Diffusion Deepfakes Using Disjoint Ensembles
Hooda, A., Mangaokar, N., Feng, R., Fawaz, K., Jha, S., & Prakash, A. (2024) · 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv57701.2024.00377 · Cited by 7 · see citing works
Pontorno, O., Guarnera, L., & Battiato, S. (2025) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-78305-0_12 · Cited by 6 · see citing works
Are CLIP Features All You Need for Universal Synthetic Image Origin Attribution?
Cioni, D., Tzelepis, C., Seidenari, L., & Patras, I. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92648-8_22 · Cited by 6 · see citing works
FIDAVL: Fake Image Detection and Attribution Using Vision-Language Model
Keïta, M., Hamidouche, W., Eutamene, H. B., Ahmed, A. T., & Hadid, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78305-0_11 · Cited by 6 · see citing works
Tran, V.-N., Choi, P., Le, H.-S., Lee, S., & Kwon, K. (2025) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2025.3575507 · Cited by 5 · see citing works
Beyond Generation: A Diffusion-based Low-level Feature Extractor for Detecting AI-generated Images
Zhong, N., Chen, H., Xu, Y., Qian, Z., & Zhang, X. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.00773 · Cited by 5 · see citing works
Generalized Image-Based Deepfake Detection Through Foundation Model Adaptation
Huang, T.-M., Han, Y.-H., Chu, E., Lo, S.-T., Hua, K., & Chen, J.-C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78305-0_13 · Cited by 5 · see citing works
X-Edit: Detecting and Localizing Edits in Images Altered by Text-Guided Diffusion Models
Bazyleva, V., Bonettini, N., & Bharaj, G. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00257 · Cited by 4 · see citing works
A Performance Analysis to Detect Synthetic Images with Deep Learning-Based Image Classification
Gautam, V., Joshi, M., Tiwari, R. G., Agarwal, A. K., & Trivedi, N. K. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-3594-5_3 · Cited by 4 · see citing works
Ahmad, W., Peng, Y., Chang, Y.-H., Ganfure, G. O., & Khan, S. (2025) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3715138 · Cited by 6 · see citing works
TextureCrop: Enhancing Synthetic Image Detection Through Texture-Based Cropping
Konstantinidou, D., Koutlis, C., & Papadopoulos, S. (2025) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw65960.2025.00160 · Cited by 5 · see citing works
Tiny Autoencoders are Effective Few-Shot Generative Model Detectors
Bindini, L., Bertazzini, G., Baracchi, D., Shullani, D., Frasconi, P., & Piva, A. (2024) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs61860.2024.10810686 · Cited by 4 · see citing works
Pixels of Faith: Exploiting Visual Saliency to Detect Religious Image Manipulation
Cartella, G., Cuculo, V., Cornia, M., Papasidero, M., Ruozzi, F., & Cucchiara, R. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91572-7_14 · Cited by 4 · see citing works
RADAR: Reasoning AI-Generated Image Detection for Semantic Fakes
Wang, H., Liu, X., Lu, Z., Yan, C., Jiang, X., Wang, R., & Gavves, E. (2025) · Technologies · doi:10.3390/technologies13070280 · Cited by 3 · see citing works
DeepFeatureX-SN: Generalization of deepfake detection via contrastive learning
Pontorno, O., Guarnera, L., & Battiato, S. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-21060-1 · Cited by 5 · see citing works
From Generation to Detection: Multimodal Generative AI and the Threat of Automated Misinformation
Kumar, R., Bansal, P., Singh, S., Saha, S., & Jatowt, A. (2026) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2026.3654905 · Cited by 3 · see citing works
Penny-Wise and Pound-Foolish in AI-Generated Image Detection
Wang, Y., Huang, Z., Su, Z., Prügel‐Bennett, A., & Hong, X. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3664388 · Cited by 3 · see citing works
Adversarial Reality for Evading Deepfake Image Detectors
Çiftçi, U. A., Solar, N., Greene, E., Saremsky, S. R., & Demir, İ. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00171 · Cited by 4 · see citing works
A destructive active defense algorithm for deepfake face images
Yang, Y., Idris, N. B., Liu, C., Wu, H., & Yu, D. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.2356 · Cited by 3 · see citing works
Choi, H., & Woo, S. S. (2025) · ACM Symposium on Applied Computing · doi:10.1145/3672608.3707733 · Cited by 3 · see citing works
Evolution of Detection Performance Throughout the Online Lifespan of Synthetic Images
Karageogiou, D., Bammey, Q., Porcellini, V., Goupil, B., Teyssou, D., & Papadopoulos, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92648-8_24 · Cited by 3 · see citing works
AI Generated Art: Latent Diffusion-Based Style and Detection
Bird, J. J., Barnes, C. M., & Lotfi, A. (2024) · Advances in intelligent systems and computing · doi:10.1007/978-3-031-47508-5_13 · Cited by 3 · see citing works
WILD: a new in-the-Wild Image Linkage Dataset for synthetic image attribution
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Chen, C., Chen, W., Lin, Y., Li, B., & Huang, J. (2025) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3719027.3744809 · Cited by 2 · see citing works
Ge, H., Zhang, W., Sun, X., Fan, Z., & Xian, Q. (2025) · Scientific Reports · doi:10.1038/s41598-025-28460-1 · Cited by 2 · see citing works
TGIF2: extended text-guided inpainting forgery dataset and benchmark
Mareen, H., Karageorgiou, D., Giakoumoglou, P., Lambert, P., Papadopoulos, S., & Wallendael, G. V. (2026) · Journal on Information Security · doi:10.1186/s13635-026-00235-9 · Cited by 2 · see citing works
Spatial-Temporal Forgery Trace Based Forgery Image Identification
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2D-FACT: Dual-Domain Fake Image Detection Against Text-to-Image Generative Models
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Exploring the Efficacy and Limitations of Histogram-Based Fake Image Detection
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Adapt to Scarcity: Few-Shot Deepfake Detection via Low-Rank Adaptation
Cappelletti, S., Baraldi, L., Cocchi, F., Cornia, M., Baraldi, L., & Cucchiara, R. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78305-0_8 · Cited by 2 · see citing works
FLODA: Harnessing Vision-Language Models for Deepfake Assessment
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A Robust Image Watermarking Approach for Application in AI Generated Images
Aditto, T. A., Shahariar, M. R., & Imtiaz, H. (2024) · International Conference on Electrical and Computer Engineering (ICECE) · doi:10.1109/icece64886.2024.11025115 · Cited by 2 · see citing works
DeepFake Detection Using CNN Models with Explainable AI Techniques
Bipin, P. R., Biju, A. K., Balagopal, M., Shyam, B., & Adithya, C. (2025) · International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS) · doi:10.1109/access65134.2025.11135663 · Cited by 3 · see citing works
- Authors
- Sha, Z., Li, Z., Yu, N., & Zhang, Y.
- Affiliations
- Helmholtz Center for Information Security (DE)
- Salesforce (United States) (US)
- Venue
- Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security
- Publisher
- ACM
- Published
- 2023-11-15
- Pages
- 3418-3432
- ISSN
- 1543-7221 (ISSN-L)
- OpenAlex
- W4388858946
- Cited by
- 175
- References
- 45
- Citations by year
- 2026: 41 · 2025: 60 · 2024: 59 · 2023: 15
- FWCI
- 18.75
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM SIGSAC Conference on Computer and Communications Security (CCS) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Steganography and Watermarking Techniques
- Keywords
- fake image detection, text-to-image generation, model attribution, generative AI misuse, Stable Diffusion, glides, latent diffusion models, image artifacts, deepfake detection
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.
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