Rich Models for Steganalysis of Digital Images

LIBSVM: A library for support vector machines

Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918

Bagging predictors

Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325

Libsvm : A library for support vector machines

Ferrari, V. (2008) · Medical Entomology and Zoology · Cited by 10,123

Information Hiding Techniques for Steganography and Digital Watermarking

Katzenbeisser, S., & Petitcolas, F. A. P. (1999) · Cited by 1,895

Optimization by Direct Search: New Perspectives on Some Classical and Modern Methods

Kolda, T. G., Lewis, R. M., & Torczon, V. J. (2003) · SIAM Review · doi:10.1137/s003614450242889 · Cited by 1,659

Feature extraction : foundations and applications

Guyon, I., Gunn, S. R., Nikravesh, M., & zadeh, L. N. (2006) · Cited by 1,658

Ensemble Classifiers for Steganalysis of Digital Media

Kodovský, J., Fridrich, J., & Holub, V. (2011) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2011.2175919 · Cited by 1,084

Using High-Dimensional Image Models to Perform Highly Undetectable Steganography

Pevný, T., Filler, T., & Bas, P. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-16435-4_13 · Cited by 1,026

Minimizing Additive Distortion in Steganography Using Syndrome-Trellis Codes

Filler, T., Judas, J., & Fridrich, J. (2011) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2011.2134094 · Cited by 968

Steganalysis by Subtractive Pixel Adjacency Matrix

Pevný, T., Bas, P., & Fridrich, J. (2010) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2010.2045842 · Cited by 899

FaceForensics++: Learning to Detect Manipulated Facial Images

Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., & Niessner, M. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00009 · Cited by 3,314

Universal distortion function for steganography in an arbitrary domain

Holub, V., Fridrich, J., & Denemark, T. (2014) · EURASIP Journal on Information Security · doi:10.1186/1687-417x-2014-1 · Cited by 1,212

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

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

Deep Residual Network for Steganalysis of Digital Images

Boroumand, M., Chen, M., & Fridrich, J. (2018) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2018.2871749 · Cited by 1,049

Multi-attentional Deepfake Detection

Zhao, H., Wei, T., Zhou, W., Zhang, W., Chen, D., & Yu, N. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00222 · Cited by 1,019

Designing steganographic distortion using directional filters

Holub, V., & Fridrich, J. (2012) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs.2012.6412655 · Cited by 1,015

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

Capsule-forensics: Using Capsule Networks to Detect Forged Images and Videos

Nguyen, H. H., Yamagishi, J., & Echizen, I. (2019) · ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2019.8682602 · Cited by 951

Structural Design of Convolutional Neural Networks for Steganalysis

Xu, G., Wu, H., & Shi, Y.-Q. (2016) · IEEE Signal Processing Letters · doi:10.1109/lsp.2016.2548421 · Cited by 882

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

Deep Learning Hierarchical Representations for Image Steganalysis

Ye, J., Ni, J., & Yi, Y. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2710946 · Cited by 865

Exploiting Visual Artifacts to Expose Deepfakes and Face Manipulations

Matern, F., Riess, C., & Stamminger, M. (2019) · 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw.2019.00020 · Cited by 824

Learning Rich Features for Image Manipulation Detection

Zhou, P., Han, X., Morariu, V. I., & Davis, L. S. (2018) · 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.2018.00116 · Cited by 855

Two-Stream Neural Networks for Tampered Face Detection

Zhou, P., Han, X., Morariu, V. I., & Davis, L. S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.229 · Cited by 700

A new cost function for spatial image steganography

Li, B., Wang, M., Huang, J., & Li, X. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7025854 · Cited by 678

Generalizing Face Forgery Detection with High-frequency Features

Luo, Y., Zhang, Y., Yan, J., & Liu, W. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01605 · Cited by 609

Constrained Convolutional Neural Networks: A New Approach Towards General Purpose Image Manipulation Detection

Bayar, B., & Stamm, M. C. (2018) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2018.2825953 · Cited by 597

Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain

Liu, H., Li, X., Zhou, W., Chen, Y., He, Y., Xue, H., Zhang, W., & Yu, N. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00083 · Cited by 600

DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection

Jiang, L., Li, R., Wu, W., Qian, C., & Loy, C. C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00296 · Cited by 599

Content-Adaptive Steganography by Minimizing Statistical Detectability

Sedighi, V., Cogranne, R., & Fridrich, J. (2015) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2015.2486744 · Cited by 575

Deep learning for steganalysis via convolutional neural networks

Qian, Y., Dong, J., Wang, W., & Tan, T. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2083479 · Cited by 573

Multi-task Learning for Detecting and Segmenting Manipulated Facial Images and Videos

Nguyen, H. H., Fang, F., Yamagishi, J., & Echizen, I. (2019) · 2019 IEEE 10th International Conference on Biometrics Theory, Applications and Systems (BTAS) · doi:10.1109/btas46853.2019.9185974 · Cited by 549

A deep learning approach to detection of splicing and copy-move forgeries in images

Rao, Y., & Ni, J. (2016) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs.2016.7823911 · Cited by 554

Noiseprint: A CNN-Based Camera Model Fingerprint

Cozzolino, D., & Verdoliva, L. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2916364 · Cited by 499

Two-Branch Recurrent Network for Isolating Deepfakes in Videos

Masi, I., Killekar, A., Mascarenhas, R. M., Gurudatt, S. P., & AbdAlmageed, W. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58571-6_39 · Cited by 471

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 521

Image steganography in spatial domain: A survey

Hussain, M., Wahab, A. W. A., Idris, Y. I. B., Ho, A. T. S., & Jung, K. (2018) · Signal Processing Image Communication · doi:10.1016/j.image.2018.03.012 · Cited by 451

Deep residual learning for image steganalysis

Wu, S., Zhong, S., & Liu, Y. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-017-4440-4 · Cited by 442

PSCC-Net: Progressive Spatio-Channel Correlation Network for Image Manipulation Detection and Localization

Liu, X., Liu, Y., Chen, J., & Liu, X. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3189545 · Cited by 469

Low-Complexity Features for JPEG Steganalysis Using Undecimated DCT

Holub, V., & Fridrich, J. (2014) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2014.2364918 · Cited by 436

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

Depth-Wise Separable Convolutions and Multi-Level Pooling for an Efficient Spatial CNN-Based Steganalysis

Zhang, R., Zhu, F., Liu, J., & Liu, G. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2936913 · Cited by 427

Automatic Steganographic Distortion Learning Using a Generative Adversarial Network

Tang, W., Tan, S., Li, B., & Huang, J. (2017) · IEEE Signal Processing Letters · doi:10.1109/lsp.2017.2745572 · Cited by 433

Detection of GAN-Generated Fake Images over Social Networks

Marra, F., Gragnaniello, D., Cozzolino, D., & Verdoliva, L. (2018) · 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr.2018.00084 · Cited by 399

Selection-channel-aware rich model for Steganalysis of digital images

Denemark, T., Sedighi, V., Holub, V., Cogranne, R., & Fridrich, J. (2014) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs.2014.7084302 · Cited by 396

DeepFake Detection by Analyzing Convolutional Traces

Guarnera, L., Giudice, O., & Battiato, S. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00341 · Cited by 374

M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection

Wang, J., Wu, Z., Ouyang, W., Han, X., Chen, J., Jiang, Y.-G., & Li, S.-N. (2022) · Proceedings of the 2022 International Conference on Multimedia Retrieval · doi:10.1145/3512527.3531415 · Cited by 368

MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation Detection

Dong, C., Chen, X., Hu, R., Cao, J., & Li, X. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3180556 · Cited by 405

Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection

Li, J., Xie, H., Li, J., Wang, Z., & Zhang, Y. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00639 · Cited by 357

Steganalysis of LSB matching using differences between nonadjacent pixels

Xia, Z., Wang, X., Sun, X., Liu, Q., & Xiong, N. N. (2014) · Multimedia Tools and Applications · doi:10.1007/s11042-014-2381-8 · Cited by 357

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

Digital image steganography using universal distortion

Holub, V., & Fridrich, J. (2013) · ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec) · doi:10.1145/2482513.2482514 · Cited by 348

A Novel Image Steganography Method via Deep Convolutional Generative Adversarial Networks

Hu, D., Wang, L., Jiang, W., Zheng, S., & Li, B. (2018) · IEEE Access · doi:10.1109/access.2018.2852771 · Cited by 348

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

Local Relation Learning for Face Forgery Detection

Chen, S., Yao, T., Chen, Y., Ding, S., Li, J., & Ji, R. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i2.16193 · Cited by 334

A New Payload Partition Strategy in Color Image Steganography

Liao, X., Yu, Y., Li, B., Li, Z., & Qin, Z. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2896270 · Cited by 330

SPAN: Spatial Pyramid Attention Network for Image Manipulation Localization

Hu, X., Zhang, Z., Jiang, Z., Chaudhuri, S., Yang, Z., & Nevatia, R. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58589-1_19 · Cited by 340

Yedroudj-Net: An Efficient CNN for Spatial Steganalysis

Yedroudj, M., Comby, F., & Chaumont, M. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8461438 · Cited by 356

DeepVision: Deepfakes Detection Using Human Eye Blinking Pattern

Jung, T., Kim, S., & Kim, K. (2020) · IEEE Access · doi:10.1109/access.2020.2988660 · Cited by 326

CNN-Based Adversarial Embedding for Image Steganography

Tang, W., Li, B., Tan, S., Barni, M., & Huang, J. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2891237 · Cited by 324

DeepFake Detection Based on Discrepancies Between Faces and Their Context

Nirkin, Y., Wolf, L., Keller, Y., & Hassner, T. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3093446 · Cited by 312

A Siamese CNN for Image Steganalysis

You, W., Zhang, H., & Zhao, X. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.3013204 · Cited by 313

Steganalysis of Adaptive JPEG Steganography Using 2D Gabor Filters

Song, X., Liu, F., Yang, C., Luo, X., & Zhang, Y. (2015) · ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec) · doi:10.1145/2756601.2756608 · Cited by 301

Steganalysis of JPEG images using rich models

Kodovský, J., & Fridrich, J. (2012) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.907495 · Cited by 297

Image Manipulation Detection by Multi-View Multi-Scale Supervision

Chen, X., Dong, C., Ji, J., Cao, J., & Li, X. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01392 · Cited by 303

Splicebuster: A new blind image splicing detector

Cozzolino, D., Poggi, G., & Verdoliva, L. (2015) · 2015 IEEE International Workshop on Information Forensics and Security (WIFS) · doi:10.1109/wifs.2015.7368565 · Cited by 283

Steganalysis of least significant bit matching using multi‐order differences

Xia, Z., Wang, X., Sun, X., & Wang, B. (2013) · Security and Communication Networks · doi:10.1002/sec.864 · Cited by 277

TruFor: Leveraging All-Round Clues for Trustworthy Image Forgery Detection and Localization

Guillaro, F., Cozzolino, D., Sud, A., Dufour, N., & Verdoliva, L. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01974 · Cited by 275

First Steps Toward Camera Model Identification With Convolutional Neural Networks

Bondi, L., Baroffio, L., Güera, D., Bestagini, P., Delp, E. J., & Tubaro, S. (2016) · IEEE Signal Processing Letters · doi:10.1109/lsp.2016.2641006 · Cited by 275

An Embedding Cost Learning Framework Using GAN

Yang, J., Ruan, D., Huang, J., Kang, X., & Shi, Y.-Q. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2922229 · Cited by 276

Random Projections of Residuals for Digital Image Steganalysis

Holub, V., & Fridrich, J. (2013) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2013.2286682 · Cited by 262

SSGAN: Secure Steganography Based on Generative Adversarial Networks

Shi, H., Dong, J., Wang, W., Qian, Y., & Zhang, X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-77380-3_51 · Cited by 253

Adaptive Payload Distribution in Multiple Images Steganography Based on Image Texture Features

Liao, X., Yin, J., Chen, M., & Qin, Z. (2020) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2020.3004708 · Cited by 252

CAT-Net: Compression Artifact Tracing Network for Detection and Localization of Image Splicing

Kwon, M.-J., Yu, I.-J., Nam, S.-H., & Lee, H.-K. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00042 · Cited by 262

Invisible steganography via generative adversarial networks

Zhang, R., Dong, S., & Liu, J. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6951-z · Cited by 266

Moving steganography and steganalysis from the laboratory into the real world

Ker, A. D., Bas, P., Böhme, R., Cogranne, R., Craver, S. A., Filler, T., Fridrich, J., & Pevný, T. (2013) · ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec) · doi:10.1145/2482513.2482965 · Cited by 247

Stacked convolutional auto-encoders for steganalysis of digital images

Tan, S., & Li, B. (2014) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipa.2014.7041565 · Cited by 245

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 284

Steganographic generative adversarial networks

Volkhonskiy, D., Nazarov, I., & Burnaev, E. V. (2020) · International Conference on Machine Vision · doi:10.1117/12.2559429 · Cited by 276

Large-Scale JPEG Image Steganalysis Using Hybrid Deep-Learning Framework

Zeng, J., Tan, S., Li, B., & Huang, J. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2779446 · Cited by 231

A Strategy of Clustering Modification Directions in Spatial Image Steganography

Li, B., Ming, W., Li, X., Tan, S., & Huang, J. (2015) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2015.2434600 · Cited by 227

Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization

Kwon, M.-J., Nam, S.-H., Yu, I.-J., Lee, H.-K., & Kim, C. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01617-5 · Cited by 248

Camera model identification with the use of deep convolutional neural networks

Tuama, A., Comby, F., & Chaumont, M. (2016) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs.2016.7823908 · Cited by 226

Explicit Visual Prompting for Low-Level Structure Segmentations

Liu, W., Shen, X., Pun, C., & Cun, 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/cvpr52729.2023.01862 · Cited by 239

DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms

Qi, H., Guo, Q., Juefei-Xu, F., Xie, X., Ma, L., Feng, W., Liu, Y., & Zhao, J. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413707 · Cited by 220

Multi-Scale Analysis Strategies in PRNU-Based Tampering Localization

Korus, P., & Huang, J. (2016) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2016.2636089 · Cited by 223

Digital image steganography: A literature survey

Mandal, P. C., Mukherjee, I., Paul, G., & Chatterji, B. N. (2022) · Information Sciences · doi:10.1016/j.ins.2022.07.120 · Cited by 205

Dual Contrastive Learning for General Face Forgery Detection

Sun, K., Yao, T., Chen, S., Ding, S., Li, J., & Ji, R. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i2.20130 · Cited by 209

Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data

Yu, N., Skripniuk, V., Abdelnabi, S., & Fritz, M. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01418 · Cited by 195

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 196

An Automatic Cost Learning Framework for Image Steganography Using Deep Reinforcement Learning

Tang, W., Li, B., Barni, M., Li, J., & Huang, J. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.3025438 · Cited by 197

Towards Robust Image Steganography

Tao, J., Li, S., Zhang, X., & Wang, Z. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2881118 · Cited by 190

Channel Attention Image Steganography With Generative Adversarial Networks

Tan, J., Liao, X., Liu, J., Cao, Y., & Jiang, H. (2021) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2021.3139671 · Cited by 197

Spatiotemporal Inconsistency Learning for DeepFake Video Detection

Gu, Z., Chen, Y., Yao, T., Ding, S., Li, J., Huang, F., & Ma, L. (2021) · Proceedings of the 29th ACM International Conference on Multimedia · doi:10.1145/3474085.3475508 · Cited by 189

JPEG-Phase-Aware Convolutional Neural Network for Steganalysis of JPEG Images

Chen, M., Sedighi, V., Boroumand, M., & Fridrich, J. (2017) · ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec) · doi:10.1145/3082031.3083248 · Cited by 187

MTD-Net: Learning to Detect Deepfakes Images by Multi-Scale Texture Difference

Yang, J., Li, A., Xiao, S., Lu, W., & Gao, X. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3102487 · Cited by 184

Scene Classification Based on Multiscale Convolutional Neural Network

Liu, Y., Zhong, Y., & Qin, Q. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2848473 · Cited by 184

Selection of Rich Model Steganalysis Features Based on Decision Rough Set $\alpha$ -Positive Region Reduction

Ma, Y., Luo, X., Li, X., Bao, Z., & Zhang, Y. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2799243 · Cited by 179

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

Steganalysis Features for Content-Adaptive JPEG Steganography

Denemark, T., Boroumand, M., & Fridrich, J. (2016) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2016.2555281 · Cited by 171

F 2 Trans: High-Frequency Fine-Grained Transformer for Face Forgery Detection

Miao, C., Tan, Z., Chu, Q., Liu, H., Hu, H., & Yu, N. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3233774 · Cited by 172

Investigation on Cost Assignment in Spatial Image Steganography

Li, B., Tan, S., Wang, M., & Huang, J. (2014) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2014.2326954 · Cited by 169

Deep learning is a good steganalysis tool when embedding key is reused for different images, even if there is a cover sourcemismatch

Pibre, L., Pasquet, J., Ienco, D., & Chaumont, M. (2016) · Electronic Imaging · doi:10.2352/issn.2470-1173.2016.8.mwsf-078 · Cited by 169

Coverless image steganography using partial-duplicate image retrieval

Zhou, Z., Mu, Y., & Wu, Q. M. J. (2018) · Soft Computing · doi:10.1007/s00500-018-3151-8 · Cited by 164

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

Identification of Various Image Operations Using Residual-Based Features

Li, H., Luo, W., Qiu, X., & Huang, J. (2016) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2016.2599849 · Cited by 165

Learning and transferring representations for image steganalysis using convolutional neural network

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