An efficient approach for copy-move image forgery detection using convolution neural network

A SIFT-Based Forensic Method for Copy–Move Attack Detection and Transformation Recovery

Amerini, I., Ballan, L., Caldelli, R., Bimbo, A. D., & Serra, G. (2011) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2011.2129512 · Cited by 1,068

Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries

Bappy, J. H., Simons, C., Nataraj, L., Manjunath, B. S., & Roy–Chowdhury, A. K. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2895466 · Cited by 493

Image Forgery Detection Using Adaptive Oversegmentation and Feature Point Matching

Pun, C., Yuan, X., & Bi, X. (2015) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2015.2423261 · Cited by 305

Pixel-Based Image Forgery Detection: A Review

Ansari, M. D., Ghrera, S. P., & Tyagi, V. (2014) · IETE Journal of Education · doi:10.1080/09747338.2014.921415 · Cited by 130

A robust technique for copy-move forgery detection and localization in digital images via stationary wavelet and discrete cosine transform

Mahmood, T., Mehmood, Z., Shah, M., & Saba, T. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2018.03.015 · Cited by 124

Copy-move and splicing image forgery detection and localization techniques: a review

Asghar, K., Habib, Z., & Hussain, M. (2016) · Australian Journal of Forensic Sciences · doi:10.1080/00450618.2016.1153711 · Cited by 113

A forgery detection algorithm for exemplar-based inpainting images using multi-region relation

Chang, I., Yu, J.-W., & Chang, C.-C. (2012) · Image and Vision Computing · doi:10.1016/j.imavis.2012.09.002 · Cited by 91

An efficient copy move forgery detection using deep learning feature extraction and matching algorithm

Agarwal, R., & Verma, O. P. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08495-z · Cited by 82

An efficient method for image forgery detection based on trigonometric transforms and deep learning

azrak, F. M. A., Sedik, A., Dessowky, M. I., El‐Banby, G. M., Khalaf, A. A. M., Elkorany, A. S., & El‐Samie, F. E. A. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08162-3 · Cited by 60

Convolutional Neural Network for Copy-Move Forgery Detection

Abdalla, Y., Iqbal, M. T., & Shehata, M. (2019) · Symmetry · doi:10.3390/sym11101280 · Cited by 58

Machine learning based medical image deepfake detection: A comparative study

Solaiyappan, S., & Wen, Y. (2022) · Machine Learning with Applications · doi:10.1016/j.mlwa.2022.100298 · Cited by 73

Enhancing Digital Image Forgery Detection Using Transfer Learning

Khalil, A. H., Ghalwash, A. Z., Abdel-Galil, H., Salama, G. I., & Ghalwash, H. A. (2023) · IEEE Access · doi:10.1109/access.2023.3307357 · Cited by 70

Image forgery techniques: a review

Kaur, G., Singh, N., & Kumar, M. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10211-7 · Cited by 61

Image forgery detection: comprehensive review of digital forensics approaches

Singh, S. P., & Kumar, R. (2024) · Journal of Computational Social Science · doi:10.1007/s42001-024-00265-8 · Cited by 58

A Survey of Detection and Mitigation for Fake Images on Social Media Platforms

Sharma, D. K., Singh, B., Agarwal, S., Garg, L., Kim, C., & Jung, K. (2023) · Applied Sciences · doi:10.3390/app131910980 · Cited by 53

Copy-Move Forgery Detection Technique Using Graph Convolutional Networks Feature Extraction

Shinde, V., Dhanawat, V., Almogren, A., Biswas, A., Bilal, M., Naqvi, R. A., & Rehman, A. U. (2024) · IEEE Access · doi:10.1109/access.2024.3452609 · Cited by 35

Design of an academic document forgery detection system

Boonkrong, S. (2024) · International Journal of Information Technology · doi:10.1007/s41870-024-02006-6 · Cited by 29

MiniNet: a concise CNN for image forgery detection

Tyagi, S., & Yadav, D. (2022) · Evolving Systems · doi:10.1007/s12530-022-09446-0 · Cited by 28

A survey on digital image forensic methods based on blind forgery detection

Shukla, D. K., Bansal, A., & Singh, P. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-023-18090-y · Cited by 20

SPA-Net: A Deep Learning Approach Enhanced Using a Span-Partial Structure and Attention Mechanism for Image Copy-Move Forgery Detection

Zhao, K., Yuan, X., Xie, Z., Xiang, Y., Huang, G., & Feng, L. (2023) · Sensors · doi:10.3390/s23146430 · Cited by 15

Detection Of Image Forgery Using Error Level Analysis

Chandana, S., Nagarathna, C. R., Amrutha, A., & Jayasri, A. (2024) · International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE) · doi:10.1109/iitcee59897.2024.10467523 · Cited by 14

A Similarity-Based Positional Attention-Aided Deep Learning Model for Copy–Move Forgery Detection

Roy, A., Mohiuddin, S., & Sarkar, R. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3379941 · Cited by 14

The hypergeometric test performs comparably to TF-IDF on standard text analysis tasks

Sheridan, P. J., & Onsjö, M. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16615-z · Cited by 17

CADEN: cellular automata and DNA based secure framework for privacy preserving in IoT based healthcare

Hurrah, N. N., Khan, E., & Khan, U. (2022) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-022-04510-8 · Cited by 11

An Investigation of Crime Detection Using Artificial Intelligence and Face Sketch Synthesis

Natarajan, R., Mahadev, N., Gupta, S. K., & Alfurhood, B. S. (2024) · Journal of Applied Security Research · doi:10.1080/19361610.2024.2302237 · Cited by 11

A comprehensive survey of audio forgery detection: challenges and novel trends

Nasr, M. A., El‐Shafai, W., Abdel-Salam, N., El‐Rabaie, E. M., El‐Fishawy, A. S., & El‐Samie, F. E. A. (2025) · Journal of Electrical Systems and Information Technology · doi:10.1186/s43067-025-00225-w · Cited by 12

Detecting Digital Image Forgeries with Copy-Move and Splicing Image Analysis using Deep Learning Techniques

Timothy, D. P., & Santra, A. K. (2024) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2024.01505131 · Cited by 9

CFDMI-SEC: An optimal model for copy-move forgery detection of medical image using SIFT, EOM and CHM

Amiri, E., Mosallanejad, A., & Sheikhahmadi, A. (2024) · PLoS ONE · doi:10.1371/journal.pone.0303332 · Cited by 8

A Thorough Investigation on Image Forgery Detection

Rai, A. K., & Srivastava, S. (2022) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2022.020920 · Cited by 7

A modified single image dehazing method for autonomous driving vision system

Kim, W., Hum, Y. C., Tee, Y. K., Yap, W., Mokayed, H., & Lai, K. W. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16547-8 · Cited by 7

Blind forgery detection using enhanced mask-region convolutional neural network

Tallapragada, V. V. S., Reddy, D. V., & Kumar, G. V. P. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19347-w · Cited by 6

Genetic Algorithm Augmented Inception-Net based Image Classifier Accelerated on FPGA

Kaziha, O., Bonny, T., & Jarndal, A. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15178-3 · Cited by 7

Detecting Copy Move Image Forgery using a Deep Learning Model: A Review

Lalli, K., Shrivastava, V. K., & Shekhar, R. (2023) · doi:10.1109/icaia57370.2023.10169568 · Cited by 6

Identification of Image Forgeries using Machine Learning - A Review

Nagarathna, C. R., Jayasri, A., Chandana, S., & Amrutha, A. (2023) · Journal of Innovative Image Processing · doi:10.36548/jiip.2023.3.007 · Cited by 5

RISAT: real-time instance segmentation with adversarial training

Pei, S., Ni, B., Shen, T., ZhenLing, Z., Chen, Y., & Meikang Qiu, A. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13447-1 · Cited by 4

Data-knowledge driven: a new learning strategy for iris recognition

Liu, S., Liu, Y., Zhu, X., & Zhang, S. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16567-4 · Cited by 4

Reversible data hiding in encrypted images using histogram modification and MSBs integration

Mohammadi, A., & Akhaee, M. A. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15083-9 · Cited by 4

A Holistic Approach to Image Forensics: Integrating Image Metadata Analysis and ELA with CNN and MLP for Image Forgery.Preprint

Nfor, K. A., Armand, T. P. T., & Kim, H. (2024) · Research Square · doi:10.21203/rs.3.rs-4667372/v1 · Cited by 4

SFTA-Net: a self-supervised approach to detect copy-move and splicing forgery to leverage triplet loss, auxiliary loss, and spatial attention

Alabrah, A. A. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.2803 · Cited by 4

Copy-Move Forgery Detection and Classification Using Improved Butterfly Optimization Algorithm-based Convolutional Neural Network

(2023) · International journal of intelligent engineering and systems · doi:10.22266/ijies2024.0229.72 · Cited by 3

Lightweight Hybrid Model Combining MobilNetV2 and PCA for Copy-Move Forgery Detection

Shallal, I., Haddada, L. R., & Amara, N. E. B. (2024) · International Conference on Developments in eSystems Engineering (DeSE) · doi:10.1109/dese63988.2024.10911910 · Cited by 3

An optimized CNN model for copy-move forgery detection and localization in digital images using particle swarm and Grey wolf optimization algorithms

Bevinamarad, P., & Unki, P. H. (2025) · Evolutionary Intelligence · doi:10.1007/s12065-025-01117-8 · Cited by 2

Copy-move document image forgery detection and localization based on JPEG clues

Chuiko, A. V., Bulatov, K. B., & Tropin, D. V. (2024) · International Conference on Machine Vision · doi:10.1117/12.3023365 · Cited by 2

Fused Deep Representation of Traditional Features for Copy-Move Forgery Detection

Khalil, A. H., Ghalwash, A. Z., Elsayed, H. A., Salama, G. I., & Ghalwash, H. A. (2024) · International Conference on Machine Intelligence and Smart Innovation (ICMISI) · doi:10.1109/icmisi61517.2024.10580100 · Cited by 2

A Novel Approach to Enhancing Identity Document Authentication with Copy-Move Forgery Detection using CNN

Devi, K. J., Dinesh, J., Neha, K. L., Teja, K. V. N. S. R., Jayashree, M., & Devi, J. (2024) · Interdisciplinary Conference on Electrics and Computer (INTCEC) · doi:10.1109/intcec61833.2024.10603169 · Cited by 2

Innovative Deep Learning Solutions for Image Forgery Detection

Mutar, M. H., Al–Fatlawy, R. R., Rasool, H. A., Ghafour, R. R., Abbas, F. H., & Hashim, D. J. (2024) · Asian Conference on Communication and Networks (ASIANComNet) · doi:10.1109/asiancomnet63184.2024.10811035 · Cited by 2

Image Copy-move Forgery Detection and Classification Using Golden Jackal Optimization Based Multi-Support Vector Machine

(2023) · International journal of intelligent engineering and systems · doi:10.22266/ijies2023.1231.11 · Cited by 2

Efficient Approach Towards Detection and Classification for Image Forgery by Using Machine Learning Technique

Hussein, A. H. A., Kavitha, R., Kumar, B. V., Issa, A. A., & Al-Attabi, K. (2023) · International Conference on Mobile Networks and Wireless Communications (ICMNWC) · doi:10.1109/icmnwc60182.2023.10435770 · Cited by 1

An Intelligent Model of Anomaly Detection and Anomaly Localization in Images Using Hybrid Heuristic Adaptive Multiscale Attention-Based DenseNet and Cascaded Variational Autoencoder

Jenifer, L. L., & Devaki, K. (2024) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001424540077 · Cited by 1

To unify or diversify: A systematic literature review of single unified models versus multiple models in detecting both copy-move and image splicing forgeries

Abou-Zbiba, W., Benbrahim, H., Bakkali, H. E., Benhaddou, D., & Gadi, H. E. (2025) · Digital Signal Processing · doi:10.1016/j.dsp.2025.105512 · Cited by 1

Recent Advances Digital Image Forgery Detection: A Systematic Mapping Study

Batista, G. D. C., Welligton, J., de Souza, M. A., Junior, W., & Kuribayashi, H. P. (2026) · IEEE Access · doi:10.1109/access.2026.3657232 · Cited by 2

A Review on Machine Learning-Based Approaches for Image Forgery Detection

Mehta, S., & Shukla, P. (2023) · Algorithms for intelligent systems · doi:10.1007/978-981-99-1435-7_8 · Cited by 1

RA-Net: A Deep Learning Approach Based on Residual Structure and Attention Mechanism for Image Copy-Move Forgery Detection

Zhao, K., Yuan, X., Xie, Z., Huang, G., & Feng, L. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44204-9_31 · Cited by 1

Automated Detection of Manipulated Images Using Deep Learning and Computer Vision Techniques

Gupta, A., & Matta, P. (2024) · International Conference on Parallel, Distributed and Grid Computing (PDGC) · doi:10.1109/pdgc64653.2024.10984289 · Cited by 1

Examining Deep Learning Models for Reliable Detection of Copy-Move Forgeries in Digital Images

Dar, N., Atsya, R., & Kumar, A. (2025) · International Conference on Computing for Sustainable Global Development · doi:10.23919/indiacom66777.2025.11115367 · Cited by 1

A Unified Approach for Copy-Move Forgery Detection Using CNN and SIFT Localization

Khan, S. A., Sravya, B. R., Debnath, S., & Soni, B. (2023) · International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON) · doi:10.1109/smartgencon60755.2023.10442553

A Comparative Study of Deep Learning and Transfer Learning Approaches for Image Forgery Detection

(2024) · REST Journal on Data Analytics and Artificial Intelligence · doi:10.46632/jdaai/3/2/18

A Review of Deep Learning Techniques for Image Forgery Detection

Ahmad, S., & Verma, B. (2024) · International Conference on Advances in Computing, Communication and Networking (ICAC2N) · doi:10.1109/icac2n63387.2024.10895541

Detection of digital image forgery using deep learning

Dakshinamurthi, V., Selvaraju, T., Gopal, V., Murali, V., & Palaniyappan, S. (2025) · AIP conference proceedings · doi:10.1063/5.0263098

Enhanced Signature Recognition and Fraud Detection with Deep Learning

Ayub, S., Shah, S. I. A., Mustansar, Z., & Khan, M. W. (2025) · International Conference on Emerging Technologies in Electronics, Computing and Communication (ICETECC) · doi:10.1109/icetecc65365.2025.11070295

ForgNetwork: An adaptive forgery detection and localization framework using stationary wavelet transform and dilated adaptive VGG16 with optimization strategy

Bevinamarad, P., & Unki, P. H. (2025) · Information Security Journal A Global Perspective · doi:10.1080/19393555.2025.2570170

A Review on Image Forgery Detection Using Deep Learning Techniques

Abdelmaksoud, M., Youssef, B., Wassif, K. T., & El-Khoribi, R. A. (2025) · International Conference on Intelligent Computing and Information Systems (ICICIS) · doi:10.1109/icicis66182.2025.11313120

Deep learning–driven authentication and forgery detection methods for oil paintings

Wang, R. (2026) · Signal Processing Image Communication · doi:10.1016/j.image.2026.117591

Research on Computer Vision Algorithms for Painting Forgery Detection

Pang, Q. (2026) · International Conference on Machine Intelligence and Digital Applications (MIDA) · doi:10.1145/3801438.3804852

Copy Move and Splicing Image Forgery Detection using CNN

Mallick, D., Shaikh, M., Gulhane, A., & Maktum, T. (2022) · ITM Web of Conferences · doi:10.1051/itmconf/20224403052 · Cited by 36

Copy-Move and Image Splicing Forgery Detection based on Convolution Neural Network

Nikalje, S., & Mane, M. V. (2022) · International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT) · doi:10.1109/icicict54557.2022.9917679 · Cited by 4

Convolutional Neural Network for Copy-Move Forgery Detection

Abdalla, Y., Iqbal, M. T., & Shehata, M. (2019) · Symmetry · doi:10.3390/sym11101280 · Cited by 58

An Efficient CNN Model to Detect Copy-Move Image Forgery

Hosny, K. M., Mortda, A. M., Fouda, M. M., & Lashin, N. A. (2022) · IEEE Access · doi:10.1109/access.2022.3172273 · Cited by 71

Metaheuristic-Assisted Convolutional Neural Network for Copy-Move Forgery Detection

Anushree, R., & B, V. K. S. (2025) · SN Computer Science · doi:10.1007/s42979-025-04242-4 · Cited by 2