Detection of GAN-Generated Fake Images over Social Networks

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works

Densely Connected Convolutional Networks

Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566 · see citing works

Going deeper with convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works

Rethinking the Inception Architecture for Computer Vision

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138 · see citing works

Unpaired image-to-image translation using cycle-consistent adversarial networks

Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602 · see citing works

Image-to-Image Translation with Conditional Adversarial Networks

Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930 · see citing works

Generative Adversarial Nets

Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,100 · see citing works

Xception: Deep Learning with Depthwise Separable Convolutions

Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380 · see citing works

GAN（Generative Adversarial Nets）

淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,268 · see citing works

Rich Models for Steganalysis of Digital Images

Fridrich, J., & Kodovsky, J. (2012) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2012.2190402 · Cited by 2,144 · see citing works

Unsupervised Image-to-Image Translation NetworksPreprint

Liu, M.-Y., Breuel, T. M., & Kautz, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.00848 · Cited by 2,121 · see citing works

Learning from Simulated and Unsupervised Images through Adversarial Training

Shrivastava, A., Pfister, T., Tuzel, O., Susskind, J., Wang, W., & Webb, R. (2017) · 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/cvpr.2017.241 · Cited by 1,831 · see citing works

Face2Face: Real-Time Face Capture and Reenactment of RGB Videos

Thies, J., Zollhofer, M., Stamminger, M., Theobalt, C., & Niessner, M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.262 · Cited by 1,747 · see citing works

A Deep Learning Approach to Universal Image Manipulation Detection Using a New Convolutional Layer

Bayar, B., & Stamm, M. C. (2016) · Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security · doi:10.1145/2909827.2930786 · Cited by 838 · see citing works

Demo of Face2Face

Thies, J., Zollhöfer, M., Stamminger, M., Theobalt, C., & Nießner, M. (2016) · ACM SIGGRAPH Emerging Technologies · doi:10.1145/2929464.2929475 · Cited by 585 · see citing works

Recasting Residual-based Local Descriptors as Convolutional Neural Networks: an Application to Image Forgery Detection

Cozzolino, D., Poggi, G., & Verdoliva, L. (2017) · Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security · doi:10.1145/3082031.3083247 · Cited by 349 · see citing works

Distinguishing computer graphics from natural images using convolution neural networks

Rahmouni, N., Nozick, V., Yamagishi, J., & Echizen, I. (2017) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs.2017.8267647 · Cited by 335 · see citing works

How realistic is photorealistic?

Lyu, S., & Farid, H. (2005) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2004.839896 · Cited by 280 · see citing works

How realistic is photorealistic

Lyu, S., & Farid, H. (2005) · doi:10.1109/tsp.2004.839896(410 · Cited by 250 · see citing works

Large-scale evaluation of splicing localization algorithms for web images

Zampoglou, M., Papadopoulos, S., & Kompatsiaris, I. Y. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-3795-2 · Cited by 167 · see citing works

Using Color Compatibility for Assessing Image Realism

Lalonde, J., & Efros, A. A. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4409107 · Cited by 153 · see citing works

Image authentication by detecting traces of demosaicing

Gallagher, A., & Chen, T. (2008) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2008.4562984 · Cited by 135 · see citing works

Can people identify original and manipulated photos of real-world scenes?

Nightingale, S. J., Wade, K. A., & Watson, D. G. (2017) · Cognitive Research: Principles and Implications · doi:10.1186/s41235-017-0067-2 · Cited by 135 · see citing works

Image forgery detection through residual-based local descriptors and block-matching

Cozzolino, D., Gragnaniello, D., & Verdoliva, L. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7026072 · Cited by 112 · see citing works

Digital Image Forensics for Identifying Computer Generated and Digital Camera Images

Dehnie, S., Sencar, T., & Memon, N. D. (2006) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2006.312849 · Cited by 94 · see citing works

New Features to Identify Computer Generated Images

Dırık, A. E., Bayram, S., Sencar, H. T., & Memon, N. D. (2007) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2007.4380047 · Cited by 81 · see citing works

Humans are easily fooled by digital images

Schetinger, V., Oliveira, M. M., da Silva, R., & Carvalho, T. (2017) · Computers & Graphics · doi:10.1016/j.cag.2017.08.010 · Cited by 64 · see citing works

Identifying computer generated graphics VIA histogram features

Wu, R., Li, X., & Yang, B. (2011) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2011.6115849 · Cited by 42 · see citing works

Assessing and Improving the Identification of Computer-Generated Portraits

Holmes, O., Banks, M. S., & Farid, H. (2016) · ACM Transactions on Applied Perception · doi:10.1145/2871714 · Cited by 41 · see citing works

Image Visual Realism: From Human Perception to Machine Computation

Fan, S., Ng, T.-T., Koenig, B. L., Herberg, J. S., Jiang, M. X., Shen, Z., & Zhao, Q. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2747150 · Cited by 37 · see citing works

Detecting Computer Generated Images with Deep Convolutional Neural Networks

Rezende, E., Ruppert, G., & Carvalho, T. (2017) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi.2017.16 · Cited by 35 · see citing works

Discrimination Between Computer Generated And Natural Human Faces Based On Asymmetry Information

Boato, G., Dang‐Nguyen, D., & Natale, F. G. B. D. (2012) · INFM-OAR (INFN Catania) · doi:10.5281/zenodo.43224 · Cited by 30 · see citing works

Distinguishing Photographic Images and Photorealistic Computer Graphics Using Visual Vocabulary on Local Image Edges

Zhang, R., Wang, R., & Ng, T.-T. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-32205-1_24 · Cited by 27 · see citing works

Calipso: physics-based image and video editing through CAD model proxies

Haouchine, N., Roy, F., Courtecuisse, H., Nießner, M., & Cotin, S. (2018) · The Visual Computer · doi:10.1007/s00371-018-1600-0 · Cited by 8 · see citing works

Face X-Ray for More General Face Forgery Detection

Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., & Guo, B. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00505 · Cited by 1,211 · see citing works

CNN-Generated Images Are Surprisingly Easy to Spot… for Now

Wang, S.-Y., Wang, O., Zhang, R., Owens, A., & Efros, A. A. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00872 · Cited by 1,210 · see citing works

Media Forensics and DeepFakes: An Overview

Verdoliva, L. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.3002101 · Cited by 979 · see citing works

Thinking in Frequency: Face Forgery Detection by Mining Frequency-Aware Clues

Qian, Y., Yin, G., Sheng, L., Chen, Z., & Shao, J. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58610-2_6 · Cited by 1,058 · see citing works

The Creation and Detection of Deepfakes: A Survey

Mirsky, Y., & Lee, W. (2020) · arXiv · doi:10.48550/arxiv.2004.11138 · Cited by 863

The Creation and Detection of Deepfakes: A Survey

Mirsky, Y., & Lee, W. (2022) · ACM Computing Surveys · doi:10.1145/3425780 · Cited by 863 · see citing works

Generative adversarial network: An overview of theory and applications

Aggarwal, A., Mittal, M., & Battineni, G. (2021) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2020.100004 · Cited by 783 · see citing works

SpotFake: A Multi-modal Framework for Fake News Detection

Singhal, S., Shah, R. R., Chakraborty, T., Kumaraguru, P., & Satoh, S. (2019) · IEEE International Conference on Multimedia Big Data · doi:10.1109/bigmm.2019.00-44 · Cited by 591 · see citing works

Detecting and Simulating Artifacts in GAN Fake Images

Zhang, X., Karaman, S., & Chang, S.-F. (2019) · 2019 IEEE International Workshop on Information Forensics and Security (WIFS) · doi:10.1109/wifs47025.2019.9035107 · Cited by 567 · see citing works

Deepfakes generation and detection: state-of-the-art, open challenges, countermeasures, and way forward

Masood, M., Nawaz, M., Malik, K. M., Javed, A., Irtaza, A., & Malik, H. (2023) · Applied Intelligence · doi:10.1007/s10489-022-03766-z · Cited by 543 · see citing works

Deep Learning for Deepfakes Creation and Detection: A Survey

Nguyen, T. T., Nguyen, Q. V. H., Nguyen, D. T., Nguyen, D. T., Huynh-The, T., Nahavandi, S., Nguyen, T. T., Pham, Q.-V., & Nguyen, C. M. (2019) · arXiv · doi:10.48550/arxiv.1909.11573 · Cited by 464

Deep learning for deepfakes creation and detection: A survey

Nguyen, T. T., Nguyen, Q. V. H., Nguyen, D. T., Nguyen, D. T., Huynh-The, T., Nahavandi, S., Nguyen, T. T., Pham, Q.-V., & Nguyen, C. M. (2022) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2022.103525 · Cited by 464 · see citing works

Watch Your Up-Convolution: CNN Based Generative Deep Neural Networks Are Failing to Reproduce Spectral Distributions

Durall, R., Keuper, M., & Keuper, J. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00791 · Cited by 26 · see citing works

Attributing Fake Images to GANs: Learning and Analyzing GAN Fingerprints

Yu, N., Davis, L. S., & Fritz, M. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00765 · Cited by 491 · see citing works

Multiple features based approach for automatic fake news detection on social networks using deep learning

Sahoo, S. R., & Gupta, B. B. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106983 · Cited by 445 · see citing works

Global Texture Enhancement for Fake Face Detection in the Wild

Liu, Z., Qi, X., & Torr, P. H. S. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00808 · Cited by 401 · see citing works

Towards Universal Fake Image Detectors that Generalize Across Generative Models

Ojha, U., Li, Y., & Lee, Y. J. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.02345 · Cited by 428 · see citing works

Detecting GAN generated Fake Images using Co-occurrence Matrices

Nataraj, L., Mohammed, T. M., Manjunath, B. S., Chandrasekaran, S., Flenner, A., Bappy, J. H., & Roy-Chowdhury, A. K. (2019) · Electronic Imaging · doi:10.2352/issn.2470-1173.2019.5.mwsf-532 · Cited by 331 · see citing works

GRAF: Generative Radiance Fields for 3D-Aware Image SynthesisPreprint

Schwarz, K., Liao, Y., Niemeyer, M., & Geiger, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.02442 · Cited by 388 · see citing works

DIRE for Diffusion-Generated Image Detection

Wang, Z., Bao, J., Zhou, W., Wang, W., Hu, H., Chen, H., & Li, H. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.02051 · Cited by 354 · see citing works

Deep fake image detection based on pairwise learning

Hsu, C.-C., Zhuang, Y.-X., & Lee, C.-Y. (2020) · Applied Sciences · doi:10.3390/app10010370 · Cited by 304 · see citing works

Do GANs leave artificial fingerprints?

Marra, F., Gragnaniello, D., Verdoliva, L., & Poggi, G. (2019) · 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr.2019.00103 · Cited by 361 · see citing works

IMD2020: A Large-Scale Annotated Dataset Tailored for Detecting Manipulated Images

Novozámský, A., Mahdian, B., & Saic, S. (2020) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw50321.2020.9096940 · Cited by 255 · see citing works

Deepfake detection by human crowds, machines, and machine-informed crowds

Groh, M., Epstein, Z., Firestone, C., & Picard, R. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2110013119 · Cited by 248 · see citing works

Identification of deep network generated images using disparities in color components

Li, H., Li, B., Tan, S., & Huang, J. (2020) · Signal Processing · doi:10.1016/j.sigpro.2020.107616 · Cited by 245 · see citing works

Detecting GAN-Generated Imagery Using Saturation Cues

McCloskey, S., & Albright, M. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803661 · Cited by 234 · see citing works

Implicit identity leakage: The stumbling block to improving deepfake detection generalization

Dong, S., Wang, J., Ji, R., Liang, J., Fan, H., & Ge, Z. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00389 · Cited by 220 · see citing works

Using AI and Social Media Multimodal Content for Disaster Response and Management: Opportunities, Challenges, and Future Directions

Imran, M., Ofli, F., Caragea, D., & Torralba, A. B. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102261 · Cited by 227 · see citing works

Hierarchical Fine-Grained Image Forgery Detection and Localization

Guo, X., Liu, X., Ren, Z., Grosz, S. A., Masi, I., & Liu, X. (2023) · 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/cvpr52729.2023.00308 · Cited by 221 · see citing works

Are GAN Generated Images Easy to Detect? A Critical Analysis of the State-Of-The-Art

Gragnaniello, D., Cozzolino, D., Marra, F., Poggi, G., & Verdoliva, L. (2021) · 2021 IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme51207.2021.9428429 · Cited by 212 · see citing works

Countering Malicious DeepFakes: Survey, Battleground, and Horizon

Juefei-Xu, F., Wang, R., Huang, Y., Guo, Q., Ma, L., & Liu, Y. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01606-8 · Cited by 191 · see citing works

Sharp Multiple Instance Learning for DeepFake Video Detection

Li, X., Lang, Y., Chen, Y., Mao, X., He, Y., Wang, S., Xue, H., & Lu, Q. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3414034 · Cited by 192 · see citing works

An improved dense cnn architecture for deepfake image detection

Patel, Y., Tanwar, S., Bhattacharya, P., Gupta, R., Alsuwian, T., Davidson, I. E., & Mazibuko, T. F. (2023) · IEEE Access · doi:10.1109/access.2023.3251417 · Cited by 196 · see citing works

Deepfake generation and detection: Case study and challenges

Patel, Y., Tanwar, S., Gupta, R., Bhattacharya, P., Davidson, I. E., Nyameko, R., Aluvala, S., & Vimal, V. (2023) · IEEE Access · doi:10.1109/access.2023.3342107 · Cited by 186 · see citing works

Deepfakes Detection with Automatic Face Weighting

Montserrat, D. M., Hao, H., Yarlagadda, S. K., Baireddy, S., Shao, R., Horvath, J., Bartusiak, E., Yang, J., Guera, D., Zhu, F., et al. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00342 · Cited by 181 · see citing works

On the Generalization of GAN Image Forensics

Xuan, X., Peng, B., Wang, W., & Dong, J. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-31456-9_15 · Cited by 184 · see citing works

Localization of Deep Inpainting Using High-Pass Fully Convolutional Network

Li, H., & Huang, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00839 · Cited by 177 · see citing works

A survey on image tampering and its detection in real-world photos

Zheng, L., Zhang, Y., & Thing, V. L. L. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2018.12.022 · Cited by 175 · see citing works

Evading Deepfake-Image Detectors with White- and Black-Box Attacks

Carlini, N., & Farid, H. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00337 · Cited by 170 · see citing works

Fake face detection via adaptive manipulation traces extraction network

Guo, Z., Yang, G., Chen, J., & Sun, X. (2021) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2021.103170 · Cited by 165 · see citing works

Incremental learning for the detection and classification of GAN-generated images

Marra, F., Saltori, C., Boato, G., & Verdoliva, L. (2019) · 2019 IEEE International Workshop on Information Forensics and Security (WIFS) · doi:10.1109/wifs47025.2019.9035099 · Cited by 154 · see citing works

CB-Fake: A multimodal deep learning framework for automatic fake news detection using capsule neural network and BERT

Palani, B., Elango, S., & Vignesh Viswanathan, K. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11782-3 · Cited by 149 · see citing works

Image Forgery Detection Using Deeplearning by Recompressing Images

Ali, S. S., Ganapathi, I. I., Vu, N.-S., Ali, S. D., Saxena, N., & Werghi, N. (2022) · Electronics · doi:10.3390/electronics11030403 · Cited by 94 · see citing works

Deepfakes Detection Techniques Using Deep Learning: A Survey

Almars, A. M. (2021) · Journal of Computer and Communications · doi:10.4236/jcc.2021.95003 · Cited by 143 · see citing works

A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detection

Marra, F., Gragnaniello, D., Verdoliva, L., & Poggi, G. (2020) · IEEE Access · doi:10.1109/access.2020.3009877 · Cited by 140 · see citing works

Combating disinformation in a social media age

Shu, K., Bhattacharjee, A., Alatawi, F., Nazer, T. H., Ding, K., Karami, M., & Liu, H. (2020) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1385 · Cited by 198 · see citing works

Intriguing properties of synthetic images: from generative adversarial networks to diffusion models

Corvi, R., Cozzolino, D., Poggi, G., Nagano, K., & Verdoliva, L. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00104 · Cited by 142 · see citing works

A Comprehensive Review of Deep-Learning-Based Methods for Image Forensics

Camacho, I. C., & Wang, K. (2021) · Journal of Imaging · doi:10.3390/jimaging7040069 · Cited by 133 · see citing works

Use of a Capsule Network to Detect Fake Images and VideosPreprint

Nguyen, H. H., Yamagishi, J., & Echizen, I. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.12467 · Cited by 163 · see citing works

Recent advances in digital image manipulation detection techniques: A brief review

Thakur, R., & Rohilla, R. (2020) · Forensic Science International · doi:10.1016/j.forsciint.2020.110311 · Cited by 129 · see citing works

Detecting GAN-generated Imagery using Color CuesPreprint

McCloskey, S., & Albright, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.08247 · Cited by 124 · see citing works

A Robust GAN-Generated Face Detection Method Based on Dual-Color Spaces and an Improved Xception

Chen, B., Liu, X., Zheng, Y., Zhao, G., & Shi, Y.-Q. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3116679 · Cited by 125 · see citing works

Deep Learning for Deepfakes Creation and Detection: A Survey

Nguyen, T. T., Nguyen, M. C., Nguyen, D. T., Nguyen, D. T., & Nahavandi, S. (2020) · Figshare · doi:10.6084/m9.figshare.12731039.v1 · Cited by 122 · see citing works

Hierarchical Frequency-Assisted Interactive Networks for Face Manipulation Detection

Miao, C.-T., Tan, Z., Chu, Q., Yu, N.-H., & Guo, G.-D. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3198275 · Cited by 110 · see citing works

Deepfake Media Forensics: Status and Future Challenges

Amerini, I., Barni, M., Battiato, S., Bestagini, P., Boato, G., Bruni, V., Caldelli, R., De Natale, F., De Nicola, R., Guarnera, L., et al. (2025) · Journal of Imaging · doi:10.3390/jimaging11030073 · Cited by 113 · see citing works

CNN Detection of GAN-Generated Face Images based on Cross-Band Co-occurrences Analysis

Barni, M., Kallas, K., Nowroozi, E., & Tondi, B. (2020) · 2020 IEEE International Workshop on Information Forensics and Security (WIFS) · doi:10.1109/wifs49906.2020.9360905 · Cited by 103 · see citing works

Deep Learning for Deepfakes Creation and Detection.Preprint

Nguyen, T. T., Nguyen, C. M., Nguyen, D. T., Nguyen, T. T., & Nahavandi, S. (2019) · arXiv (Cornell University) · Cited by 97 · see citing works

A Survey of Deep Learning-Based Source Image Forensics

Yang, P., Baracchi, D., Ni, R., Zhao, Y., Argenti, F., & Piva, A. (2020) · Journal of Imaging · doi:10.3390/jimaging6030009 · Cited by 116 · see citing works

Comprehensive analyses of image forgery detection methods from traditional to deep learning approaches: an evaluation

Sharma, P., Kumar, M., & Sharma, H. K. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13808-w · Cited by 90 · see citing works

Detecting fake images by identifying potential texture difference

Yang, J., Xiao, S., Li, A., Lan, G., & Wang, H. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.06.043 · Cited by 86 · see citing works

A review on fake news detection 3T’s: typology, time of detection, taxonomies

Rastogi, S., & Bansal, D. (2022) · International Journal of Information Security · doi:10.1007/s10207-022-00625-3 · Cited by 93 · see citing works

A temporal ensembling based semi-supervised ConvNet for the detection of fake news articles

Meel, P., & Vishwakarma, D. K. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115002 · Cited by 85 · see citing works

Unsupervised Fake News Detection

Gangireddy, S. C. R., Deepak, P., Long, C., & Chakraborty, T. (2020) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/3372923.3404783 · Cited by 80 · see citing works

Copy-Move Forgery Detection and Localization Using a Generative Adversarial Network and Convolutional Neural-Network

Abdalla, Y., Iqbal, M. T., & Shehata, M. (2019) · Information · doi:10.3390/info10090286 · Cited by 74 · see citing works

Detection of Fake Images Via The Ensemble of Deep Representations from Multi Color Spaces

He, P., Li, H., & Wang, H. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803740 · Cited by 72 · see citing works

Think Twice Before Detecting GAN-generated Fake Images from their Spectral Domain Imprints

Dong, C., Kumar, A., & Liu, E. (2022) · 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/cvpr52688.2022.00771 · Cited by 72 · see citing works

Locally GAN-generated face detection based on an improved Xception

Chen, B., Ju, X., Xiao, B., Ding, W., Zheng, Y., & de Albuquerque, V. H. C. (2021) · Information Sciences · doi:10.1016/j.ins.2021.05.006 · Cited by 70 · see citing works

Optimization of a Pre-Trained AlexNet Model for Detecting and Localizing Image Forgeries

Samir, S., Emary, E., El-Sayed, K., & Onsi, H. M. (2020) · Information · doi:10.3390/info11050275 · Cited by 67 · see citing works

Untitled work

(2020) · doi:10.1109/cvprw50498.2020.00329 · Cited by 18 · see citing works

Beyond the Spectrum: Detecting Deepfakes via Re-Synthesis

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An Exploratory Analysis on Visual Counterfeits Using Conv-LSTM Hybrid Architecture

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GLFF: Global and Local Feature Fusion for AI-Synthesized Image Detection

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Towards Discovery and Attribution of Open-world GAN Generated Images

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Identifying and Mitigating the Security Risks of Generative AI

Barrett, C., Boyd, B., Bursztein, E., Carlini, N., Chen, B., Choi, J., Chowdhury, A. R., Christodorescu, M., Datta, A., Feizi, S., et al. (2023) · Foundations and Trends® in Privacy and Security · doi:10.1561/3300000041 · Cited by 57 · see citing works

Improving Video Vision Transformer for Deepfake Video Detection Using Facial Landmark, Depthwise Separable Convolution and Self Attention

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Preliminary Forensics Analysis of DeepFake Images

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LaRE 2 : Latent Reconstruction Error Based Method for Diffusion-Generated Image Detection

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Deepfake attribution: On the source identification of artificially generated images

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Improving EfficientNet for JPEG Steganalysis

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ConvNet frameworks for multi-modal fake news detection

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A systematic literature review on deepfake detection techniques

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SpoC: Spoofing Camera Fingerprints

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Dynamic texture analysis for detecting fake faces in video sequences

Bonomi, M., Pasquini, C., & Boato, G. (2021) · Institutional Research Information System (Università degli Studi di Trento) · doi:10.1016/j.jvcir.2021.103239 · Cited by 51 · see citing works

On the Frequency Bias of Generative ModelsPreprint

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FFR_FD: Effective and fast detection of DeepFakes via feature point defects

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Deepfake Video Detection Using Convolutional Neural Network

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Deepfake Detection Using Spatiotemporal Transformer

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“Combating deepfakes: multi-LSTM and blockchain as proof of authenticity for digital media,”

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ForgeryNIR: Deep Face Forgery and Detection in Near-Infrared Scenario

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Learned Spatial Representations for Few-shot Talking-Head Synthesis

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Multimodal fake news detection using a Cultural Algorithm with situational and normative knowledge

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Analysis of Adversarial Attacks against CNN-based Image Forgery Detectors

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EFND: A Semantic, Visual, and Socially Augmented Deep Framework for Extreme Fake News Detection

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Visual Deepfake Detection: Review of Techniques, Tools, Limitations, and Future Prospects

Ur Rehman Ahmed, N., Badshah, A., Adeel, H., Tajammul, A., Daud, A., & Alsahfi, T. (2025) · IEEE Access · doi:10.1109/access.2024.3523288 · Cited by 45 · see citing works

Leveraging Representations from Intermediate Encoder-Blocks for Synthetic Image Detection

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Multi-modal fusion using Fine-tuned Self-attention and transfer learning for veracity analysis of web information

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Detection of Real Versus Fake Images on Social Media Through Generative Adversarial Networks

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Detection and Identification of Fake Images using Conditional Generative Adversarial Networks (CGANs)

Mohebbanaaz, Jyothirmai, M., Mounika, K., Sravani, E., & Mounika, B. (2024) · International Conference on Computational Intelligence and Communication Networks (CICN) · doi:10.1109/cicn63059.2024.10847379 · Cited by 29 · see citing works

Deep Fake Image Detection based on Pairwise Learning

Hsu, C., Zhuang, Y.-X., & Lee, C. (2019) · Cited by 1 · see citing works

GAN-Deepfakes Detection Using ELA and Deep Learning

Aissa, F. B., Chaibi, N., Mejdoub, M., & Zaied, M. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-78925-0_8

Detecting GAN generated Fake Images using Co-occurrence Matrices

Nataraj, L., Mohammed, T. M., Manjunath, B. S., Chandrasekaran, S., Flenner, A., Bappy, J. H., & Roy-Chowdhury, A. K. (2019) · Electronic Imaging · doi:10.2352/issn.2470-1173.2019.5.mwsf-532 · Cited by 331 · see citing works