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

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

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

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

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

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

Generative Adversarial Nets

Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,119

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 20,507

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

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 2,194

A Comparative Study of Deepfake Video Detection Method

Ramadhani, K. N., & Munir, R. (2020) · 2020 3rd International Conference on Information and Communications Technology (ICOIACT) · doi:10.1109/icoiact50329.2020.9331963 · Cited by 42

Detection of AI-Generated Synthetic Faces

Gragnaniello, D., Marra, F., & Verdoliva, L. (2022) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-030-87664-7_9 · Cited by 40

Attributing Fake Images to GANs: Learning and Analyzing GAN FingerprintsPreprint

Yu, N., Davis, L. S., & Fritz, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.08180 · Cited by 38

Beyond Deepfake Images: Detecting AI-Generated Videos

Vahdati, D. S., Nguyen, T. D., Azizpour, A., & Stamm, M. C. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00443 · Cited by 37

A multi-modal approach towards mining social media data during natural disasters - A case study of Hurricane Irma

Mohanty, S. D., Biggers, B., Sayedahmed, S., Pourebrahim, N., Goldstein, E. B., Bunch, R. L., Chi, G., Sadri, F., McCoy, T., & Cosby, A. G. (2021) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2020.102032 · Cited by 37

DeepFake Creation and Detection:A Survey

Swathi, P. S., & Saritha, S. K. (2021) · International Conference on Inventive Research in Computing Applications (ICIRCA) · doi:10.1109/icirca51532.2021.9544522 · Cited by 37

Video surveillance in smart cities: current status, challenges & future directions

Sharma, H., & Kanwal, N. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19696-6 · Cited by 36

Artifact: A Large-Scale Dataset With Artificial And Factual Images For Generalizable And Robust Synthetic Image Detection

Rahman, M. A., Paul, B., Sarker, N. H., Hakim, Z. I. A., & Fattah, S. A. (2023) · 2023 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip49359.2023.10222083 · Cited by 34

A Representative Study on Human Detection of Artificially Generated Media Across Countries

Frank, J., Herbert, F., Ricker, J., Schönherr, L., Eisenhofer, T., Fischer, A., Dürmuth, M., & Holz, T. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00159 · Cited by 66

Digital Face Manipulation Creation and Detection: A Systematic Review

Dang, M. H., & Nguyen, T. N. (2023) · Electronics · doi:10.3390/electronics12163407 · Cited by 39

No One Can Escape: A General Approach to Detect Tampered and Generated Image

Zhang, K., Yu, L., Zhang, J., Wang, Z., & Li, X. (2019) · IEEE Access · doi:10.1109/access.2019.2939812 · Cited by 34

CSC-Net: Cross-Color Spatial Co-Occurrence Matrix Network for Detecting Synthesized Fake Images

Qiao, T., Chen, Y., Zhou, X., Shi, R., Shao, H., Shen, K., & Luo, X. (2023) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2023.3274450 · Cited by 33

Language-Guided Hierarchical Fine-Grained Image Forgery Detection and Localization

Guo, X. B., Liu, X., Masi, I., & Liu, X. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02255-9 · Cited by 38

Discovering Transferable Forensic Features for CNN-Generated Images Detection

Chandrasegaran, K., Tran, N.-T., Binder, A., & Cheung, N. M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19784-0_39 · Cited by 32

Visual User-Generated Content Verification in Journalism: An Overview

Khan, S. A., Sheikhi, G., Opdahl, A. L., Rabbi, F., Stoppel, S., Trattner, C., & Dang‐Nguyen, D. (2023) · IEEE Access · doi:10.1109/access.2023.3236993 · Cited by 36

Source Generator Attribution via InversionPreprint

Albright, M., & McCloskey, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.02259 · Cited by 30

NoiseScope: Detecting Deepfake Images in a Blind Setting

Pu, J., Mangaokar, N., Wang, B., Reddy, C. K. A., & Viswanath, B. (2020) · Annual Computer Security Applications Conference · doi:10.1145/3427228.3427285 · Cited by 31

Detection, Attribution and Localization of GAN Generated Images

Goebel, M. G., Nataraj, L., Nanjundaswamy, T., Mohammed, T. M., Chandrasekaran, S., & Manjunath, B. S. (2021) · Electronic Imaging · doi:10.2352/issn.2470-1173.2021.4.mwsf-276 · Cited by 41

CYBORG: Blending Human Saliency Into the Loss Improves Deep Learning-Based Synthetic Face Detection

Boyd, A., Tinsley, P., Bowyer, K. W., & Czajka, A. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00605 · Cited by 30

Discovering Image Manipulation History by Pairwise Relation and Forensics Tools

Zhang, X., Sun, Z. H., Karaman, S., & Chang, S. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.2999827 · Cited by 29

Distinguishing natural and computer generated images using Multi-Colorspace fused EfficientNet

Gangan, M. P., Anoop, K. R., & Lajish, V. L. (2022) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2022.103261 · Cited by 31

LW-DeepFakeNet: a lightweight time distributed CNN-LSTM network for real-time DeepFake video detection

Masud, U., Sadiq, M., Masood, S., Ahmad, M., & El‐Latif, A. A. A. (2023) · Signal Image and Video Processing · doi:10.1007/s11760-023-02633-9 · Cited by 30

Detecting Generated Image Based on a Coupled Network with Two-Step Pairwise Learning

Zhuang, Y.-X., & Hsu, C. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803464 · Cited by 28

Extracting Deep Local Features to Detect Manipulated Images of Human Faces

Tarasiou, M., & Zafeiriou, S. P. (2020) · Proceedings - International Conference on Image Processing · doi:10.1109/icip40778.2020.9190714 · Cited by 29

DeePhy: On Deepfake Phylogeny

Narayan, K., Agarwal, H., Thakral, K., Mittal, S., Vatsa, M., & Singh, R. (2022) · 2022 IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb54206.2022.10007968 · Cited by 26

Generative Adversarial Network (GAN)-Based Autonomous Penetration Testing for Web Applications

Chowdhary, A., Jha, K., & Zhao, M. X. (2023) · Sensors · doi:10.3390/s23188014 · Cited by 26

Unmasking AI-created visual content: a review of generated images and deepfake detection technologies

Zhang, Y., Pang, Z., Huang, S., Wang, C., & Zhou, X. (2025) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-025-00154-8 · Cited by 36

A Novel Neural Model based Framework for Detection of GAN Generated Fake Images

Agarwal, S., Girdhar, N., & Raghav, H. (2021) · International Conference on Cloud Computing, Data Science & Engineering (Confluence) · doi:10.1109/confluence51648.2021.9377150 · Cited by 25

Tensor-Based Deepfake Detection in Scaled and Compressed Images

Concas, S., Perelli, G., Marcialis, G. L., & Puglisi, G. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9897606 · Cited by 25

How to “inoculate” against multimodal misinformation: A conceptual replication of Roozenbeek and van der Linden (2020)

Neylan, J., Biddlestone, M., Roozenbeek, J., & van der Linden, S. (2023) · Scientific Reports · doi:10.1038/s41598-023-43885-2 · Cited by 33

Evaluation of synthetic aerial imagery using unconditional generative adversarial networks

Yates, M., Hart, G., Houghton, R. J., Torres, M. T., & Pound, M. P. (2022) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.06.010 · Cited by 24

VIPPrint: Validating Synthetic Image Detection and Source Linking Methods on a Large Scale Dataset of Printed Documents

Ferreira, A., Nowroozi, E., & Barni, M. (2021) · Journal of Imaging · doi:10.3390/jimaging7030050 · Cited by 23

Detection of Manipulated Face Videos over Social Networks: A Large-Scale Study

Marcon, F., Pasquini, C., & Boato, G. (2021) · Journal of Imaging · doi:10.3390/jimaging7100193 · Cited by 24

Exposing unseen GAN-generated image using unsupervised domain adaptation

Zhang, M., Wang, H., He, P., Malik, A., & Liu, H. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109905 · Cited by 23

Jointly Defending DeepFake Manipulation and Adversarial Attack Using Decoy Mechanism

Chen, G., & Hsu, C. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3253390 · Cited by 24

GAN-based medical image small region forgery detection via a two-stage cascade framework

Zhang, J., Huang, X., Liu, Y., Han, Y., & Xiang, Z. (2024) · PLoS ONE · doi:10.1371/journal.pone.0290303 · Cited by 25

Generalized Facial Manipulation Detection with Edge Region Feature Extraction

Kim, D.-K., & Kim, K. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00284 · Cited by 22

$$\text {Face2Face}^\rho $$: Real-Time High-Resolution One-Shot Face Reenactment

Yang, K., Chen, K., Guo, D., Zhang, S., Guo, Y.-C., & Zhang, W. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19778-9_4 · Cited by 23

News authentication and tampered images: evaluating the photo-truth impact through image verification algorithms

Katsaounidou, A. N., Gardikiotis, A., Tsipas, N., & Dimoulas, C. A. (2020) · Heliyon · doi:10.1016/j.heliyon.2020.e05808 · Cited by 22

Recaptured screen image identification based on vision transformer

Li, G., Yao, H., Le, Y., & Qin, C. (2022) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2022.103692 · Cited by 21

MITS-GAN: Safeguarding medical imaging from tampering with generative adversarial networks

Pasqualino, G., Guarnera, L., Ortis, A., & Battiato, S. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109248 · Cited by 23

A survey of intelligent multimedia forensics for internet of things communications: Approaches, strategies, perspectives, and challenges for a sustainable future

Ding, W., Abdel‐Basset, M., Ali, A. M., & Moustafa, N. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109451 · Cited by 24

Modeling strategies for information influence dissemination in social networks

Al-Oraiqat, A. M., Ulichev, O. S., Мелешко, Є. В., Al-Rawashdeh, H. S., Smirnov, O. A., & Polishchuk, L. I. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03364-w · Cited by 20

Prediction of face age progression with generative adversarial networks

Sharma, N., Sharma, R., & Jindal, N. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11252-w · Cited by 22

AI-Generated Face Image Identification with Different Color Space Channel Combinations

Mo, S., Lü, P., & Liu, X. (2022) · Sensors · doi:10.3390/s22218228 · Cited by 19

A-PixelHop: A Green, Robust and Explainable Fake-Image Detector

Zhu, Y. C., Wang, X., Chen, H.-S., Salloum, R., & Kuo, C. ‐. J. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747901 · Cited by 19

Face Aging with Conditional Generative Adversarial Network Guided by Ranking-CNN

Sheng, M., Ma, Z., Jia, H.-J., Mao, Q.-R., & Dong, M. (2020) · Conference on Multimedia Information Processing and Retrieval · doi:10.1109/mipr49039.2020.00071 · Cited by 11

Deepfake Detection Using Multiple Data Modalities

Hao, H., Bartusiak, E. R., Güera, D., Montserrat, D. M., Baireddy, S., Xiang, Z., Yarlagadda, S. K., Shao, R., Horváth, J., Yang, J., et al. (2022) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-030-87664-7_11 · Cited by 20

An Eyes-Based Siamese Neural Network for the Detection of GAN-Generated Face Images

Wang, J., Tondi, B., & Barni, M. (2022) · Frontiers in Signal Processing · doi:10.3389/frsip.2022.918725 · Cited by 19

Analysis of Score-Level Fusion Rules for Deepfake Detection

Concas, S., Cava, S. M. L., Orrù, G., Cuccu, C., Gao, J., Feng, X., Marcialis, G. L., & Roli, F. (2022) · Applied Sciences · doi:10.3390/app12157365 · Cited by 21

Deepfake-Image Anti-Forensics with Adversarial Examples Attacks

Fan, L., Li, W., & Cui, X. (2021) · Future Internet · doi:10.3390/fi13110288 · Cited by 17

Overview of Image Inpainting and Forensic Technology

Liu, K., Li, J., & Bukhari, S. S. H. (2022) · Security and Communication Networks · doi:10.1155/2022/9291971 · Cited by 19

Stop the [Image] Steal

Matatov, H., Naaman, M., & Amir, O. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555599 · Cited by 17

Cyber Vaccine for Deepfake Immunity

Chang, C., Nguyen, H. H., Yamagishi, J., & Echizen, I. (2023) · IEEE Access · doi:10.1109/access.2023.3311461 · Cited by 20

AI-Generated Faces in the Real World: A Large-Scale Case Study of Twitter Profile Images

Ricker, J., Assenmacher, D., Holz, T., Fischer, A., & Quiring, E. (2024) · The 27th International Symposium on Research in Attacks, Intrusions and Defenses · doi:10.1145/3678890.3678922 · Cited by 22

Protecting the trust and credibility of data by tracking forgery trace based on GANs

Xiao, S., Yang, J., & Lv, Z. (2022) · Digital Communications and Networks · doi:10.1016/j.dcan.2022.07.010 · Cited by 16

Data augmentation with attention framework for robust deepfake detection

Mamarasulov, S., Chen, L., Chen, C., Li, Y., & Wang, C. (2024) · The Visual Computer · doi:10.1007/s00371-024-03690-y · Cited by 16

Deepfake Media Forensics: State of the Art and Challenges Ahead

Amerini, I., Barni, M., Battiato, S., Bestagini, P., Boato, G., Bonaventura, T. S., Bruni, V., Caldelli, R., Natale, F. G. B. D., Nicola, R. D., et al. (2025) · Lecture notes in social networks · doi:10.1007/978-3-031-85386-9_3 · Cited by 32

Evaluation of Pre-Trained CNN Models for Geographic Fake Image Detection

Fezza, S. A., Ouis, M. Y., Kaddar, B., Hamidouche, W., & Hadid, A. (2022) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp55362.2022.9949282 · Cited by 17

A secure model on Advanced Fake Image-Feature Network (AFIFN) based on deep learning for image forgery detection

Ananthi, M., Rajkumar, P. R., Sabitha, R., & Karthik, S. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.10.011 · Cited by 15

ZeroFake: Zero-Shot Detection of Fake Images Generated and Edited by Text-to-Image Generation Models

Sha, Z., Tan, Y., Li, M., Michael Backes, A., & Zhang, Y. (2024) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3658644.3690297 · Cited by 19

A Unified Neural Framework for Real-Time Deepfake Detection Across Multimedia Modalities to Combat Misleading Content

Sar, A., Sati, S., Choudhury, T., Joshi, P., Sille, R., Srihari, K., & Bansal, K. (2025) · IEEE Access · doi:10.1109/access.2025.3550770 · Cited by 19

Detection of GAN generated image using color gradient representation

Liu, Y., Wan, Z., Yin, X., Yue, G., Tan, A., & Zheng, Z. (2023) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2023.103876 · Cited by 15

On the effectiveness of handcrafted features for deepfake video detection

Kaddar, B., Fezza, S. A., Hamidouche, W., Akhtar, Z., Hadid, A., Serra-Sagrista, J. S., Hernández-Cabronero, M., & Sanchez, V. (2023) · Journal of Electronic Imaging · doi:10.1117/1.jei.32.5.053033 · Cited by 15

Detecting GAN-generated face images via hybrid texture and sensor noise based features

Fu, T., Xia, M., & Yang, G. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12661-1 · Cited by 15

AR-NeRF: Unsupervised Learning of Depth and Defocus Effects from Natural Images with Aperture Rendering Neural Radiance Fields

Kaneko, T. (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/cvpr52688.2022.01784 · Cited by 15

GANprintR: Improved Fakes and Evaluation of the State of the Art in Face Manipulation Detection

Neves, J. C., Tolosana, R., Vera-Rodriguez, R., Lopes, V., Proenca, H., & Fierrez, J. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.3007250 · Cited by 12

Black-box attack against GAN-generated image detector with contrastive perturbation

Lou, Z., Cao, G., & Lin, M. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106594 · Cited by 13

Robust GAN-Based CNN Model as Generative AI Application for Deepfake Detection

Sharma, P., Kumar, M., & Sharma, H. K. (2024) · EAI Endorsed Transactions on Internet of Things · doi:10.4108/eetiot.5637 · Cited by 13

Anisotropic multiresolution analyses for deepfake detection

Huang, W., Valsecchi, M., & Multerer, M. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111551 · Cited by 14

Analysis of the current state of deepfake techniques-creation and detection methods

Abu-Ein, A. A.-K. H., Al-Hazaimeh, O. M., Dawood, A. M., & Swidan, A. I. (2022) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v28.i3.pp1659-1667 · Cited by 16

Perceptual Artifacts Localization for Inpainting

Zhang, L., Zhou, Y., Barnes, C., Amirghodsi, S., Lin, Z., Shechtman, E., & Shi, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19818-2_9 · Cited by 13

TrueFace: a Dataset for the Detection of Synthetic Face Images from Social Networks

Boato, G., Pasquini, C., Stefani, A. L., Verde, S., & Miorandi, D. (2022) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb54206.2022.10007988 · Cited by 15

Medical Image Tampering Detection: A New Dataset and Baseline

Reichman, B., Jing, L., Akin, O., & Tian, Y. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-68763-2_20 · Cited by 13

MRDCA: a multimodal approach for fine-grained fake news detection through integration of RoBERTa and DenseNet based upon fusion mechanism of co-attention

Qian, L., Xu, R., & Zhou, Z. (2022) · Annals of Operations Research · doi:10.1007/s10479-022-05154-9 · Cited by 12

Forgery Detection by Weighted Complementarity between Significant Invariance and Detail Enhancement

Xiao, S., Zhang, Z., Yang, J., Wen, J., & Li, Y. (2023) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3605893 · Cited by 13

A Robust Open-Set Multi-Instance Learning for Defending Adversarial Attacks in Digital Image

Uddin, K., Yang, Y., Jeong, T. H., & Oh, B. T. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3346211 · Cited by 12

A Comprehensive Review on Fake Images/Videos Detection Techniques

Chauhan, R., Popli, R., & Kansal, I. (2022) · International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) · doi:10.1109/icrito56286.2022.9964871 · Cited by 12

DeepFake Detection Algorithms: A Meta-Analysis

Zotov, S., Dremliuga, R., Borshevnikov, A., & Krivosheeva, K. (2020) · 2020 2nd Symposium on Signal Processing Systems · doi:10.1145/3421515.3421532 · Cited by 12

Drone‐Assisted Image Forgery Detection Using Generative Adversarial Net‐Based Module

Gowroju, S., Choudhary, S., Rishitha, M., Tejaswi, S., Reddy, L. S., & Reddy, M. S. (2024) · doi:10.1002/9781394175512.ch11 · Cited by 11

Deepfake Detection of Occluded Images Using a Patch-Based Approach

Soleimani, M., Nazari, A., & Moghaddam, M. E. (2022) · Multimedia Systems · doi:10.1007/s00530-023-01140-8 · Cited by 14

Towards Robust Gan-Generated Image Detection: A Multi-View Completion Representation

Liu, C., Zhu, T., Shen, S., & Zhou, W. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/52 · Cited by 10

Data-agnostic Face Image Synthesis Detection using Bayesian CNNs

Leyva, R., Sánchez, V., Epiphaniou, G., & Maple, C. R. (2024) · Pattern Recognition Letters · doi:10.1016/j.patrec.2024.04.008 · Cited by 11

SA$$^3$$WT: Adaptive Wavelet-Based Transformer with Self-Paced Auto Augmentation for Face Forgery Detection

Li, Y., Zhang, Y., Yang, H., Chen, B., & Huang, D. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02091-x · Cited by 10

Enhancing Interpretability in AI-Generated Image Detection with Genetic Programming

Lin, M., Shang, L., & Gao, X. (2023) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw60847.2023.00053 · Cited by 10

Evading generated-image detectors: A deep dithering approach

Xie, H., Ni, J., Zhang, J., Zhang, W., & Huang, J. (2022) · Signal Processing · doi:10.1016/j.sigpro.2022.108558 · Cited by 11

One-Shot GAN Generated Fake Face DetectionPreprint

Mansourifar, H., & Shi, W. L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.12244 · Cited by 10

Fake Face Detection via Adaptive Residuals Extraction NetworkPreprint

Guo, Z., Yang, G., Chen, J., & Sun, X. (2020) · arXiv (Cornell University) · Cited by 10

CSTAN: A Deepfake Detection Network with CST Attention for Superior Generalization

Yang, R., You, K., Pang, C., Luo, X., & Lan, R. (2024) · Sensors · doi:10.3390/s24227101 · Cited by 9

HateProof: Are Hateful Meme Detection Systems really Robust?

Aggarwal, P., Chawla, P., Das, M., Saha, P., Mathew, B., Zesch, T., & Mukherjee, A. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583356 · Cited by 9

Exposing AI Generated Deepfake Images Using Siamese Network with Triplet Loss

Kanwal, S., Tehsin, S., & Saif, S. (2022) · Computing and Informatics · doi:10.31577/cai_2022_6_1541 · Cited by 10

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 17

Towards the Detection of AI-Synthesized Human Face Images

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