Research on Multimodal Image Tampering Detection and Counterfeit Image Recognition Techniques under Deep Learning Framework

[1] Pang, Y., Li, X., Wei, S., Li, Q., & Xiao, Y. (2023). Topic to image: A rumor detection method inspired by image forgery recognition technology. IEEE Transactions on Computational Social Systems. Pang Y. Li X. Wei S. Li Q. Xiao Y. ( 2023 ). Topic to image: A rumor detection method inspired by image forgery recognition technology . IEEE Transactions on Computational Social Systems . Search in Google Scholar

[2] Saber, A. H., Khan, M. A., & Mejbel, B. G. (2020). A survey on image forgery detection using different forensic approaches. Advances in Science, Technology and Engineering Systems Journal, 5(3), 361-370. Saber A. H. Khan M. A. Mejbel B. G. ( 2020 ). A survey on image forgery detection using different forensic approaches . Advances in Science, Technology and Engineering Systems Journal , 5 ( 3 ), 361 - 370 . Search in Google Scholar

[3] Zanardelli, M., Guerrini, F., Leonardi, R., & Adami, N. (2023). Image forgery detection: a survey of recent deep-learning approaches. Multimedia Tools and Applications, 82(12), 17521-17566. Zanardelli M. Guerrini F. Leonardi R. Adami N. ( 2023 ). Image forgery detection: a survey of recent deep-learning approaches . Multimedia Tools and Applications , 82 ( 12 ), 17521 - 17566 . Search in Google Scholar

[4] Elaskily, M. A., Elnemr, H. A., Dessouky, M. M., & Faragallah, O. S. (2019). Two stages object recognition based copy-move forgery detection algorithm. Multimedia Tools and Applications, 78, 15353-15373. Elaskily M. A. Elnemr H. A. Dessouky M. M. Faragallah O. S. ( 2019 ). Two stages object recognition based copy-move forgery detection algorithm . Multimedia Tools and Applications , 78 , 15353 - 15373 . Search in Google Scholar

[5] Sudiatmika, I. B. K., Rahman, F., Trisno, T., & Suyoto, S. (2019). Image forgery detection using error level analysis and deep learning. TELKOMNIKA (Telecommunication Computing Electronics and Control), 17(2), 653-659. Sudiatmika I. B. K. Rahman F. Trisno T. Suyoto S. ( 2019 ). Image forgery detection using error level analysis and deep learning . TELKOMNIKA (Telecommunication Computing Electronics and Control) , 17 ( 2 ), 653 - 659 . Search in Google Scholar

[6] Mehrjardi, F. Z., Latif, A. M., Zarchi, M. S., & Sheikhpour, R. (2023). A survey on deep learning-based image forgery detection. Pattern Recognition, 109778. Mehrjardi F. Z. Latif A. M. Zarchi M. S. Sheikhpour R. ( 2023 ). A survey on deep learning-based image forgery detection . Pattern Recognition , 109778 . Search in Google Scholar

[7] Marra, F., Gragnaniello, D., Verdoliva, L., & Poggi, G. (2020). A full-image full-resolution end-to-end-trainable CNN framework for image forgery detection. IEEE Access, 8, 133488-133502. Marra F. Gragnaniello D. Verdoliva L. Poggi G. ( 2020 ). A full-image full-resolution end-to-end-trainable CNN framework for image forgery detection . IEEE Access , 8 , 133488 - 133502 . Search in Google Scholar

[8] Poddar, J., Parikh, V., & Bharti, S. K. (2020). Offline signature recognition and forgery detection using deep learning. Procedia Computer Science, 170, 610-617. Poddar J. Parikh V. Bharti S. K. ( 2020 ). Offline signature recognition and forgery detection using deep learning . Procedia Computer Science , 170 , 610 - 617 . Search in Google Scholar

[9] Cozzolino, D., Poggi, G., & Verdoliva, L. (2017, June). Recasting residual-based local descriptors as convolutional neural networks: an application to image forgery detection. In Proceedings of the 5th ACM workshop on information hiding and multimedia security (pp. 159-164). Cozzolino D. Poggi G. Verdoliva L. ( 2017 , June ). Recasting residual-based local descriptors as convolutional neural networks: an application to image forgery detection . In Proceedings of the 5th ACM workshop on information hiding and multimedia security (pp. 159 - 164 ). Search in Google Scholar

[10] Kaur, G., Singh, N., & Kumar, M. (2023). Image forgery techniques: a review. Artificial Intelligence Review, 56(2), 1577-1625. Kaur G. Singh N. Kumar M. ( 2023 ). Image forgery techniques: a review . Artificial Intelligence Review , 56 ( 2 ), 1577 - 1625 . Search in Google Scholar

[11] Su, G. D., Chang, C. C., & Chen, C. C. (2021). A hybrid-Sudoku based fragile watermarking scheme for image tampering detection. Multimedia Tools and Applications, 80, 12881-12903. Su G. D. Chang C. C. Chen C. C. ( 2021 ). A hybrid-Sudoku based fragile watermarking scheme for image tampering detection . Multimedia Tools and Applications , 80 , 12881 - 12903 . Search in Google Scholar

[12] Singh, P., Raman, B., Agarwal, N., & Atrey, P. K. (2017). Secure cloud-based image tampering detection and localization using POB number system. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 13(3), 1-23. Singh P. Raman B. Agarwal N. Atrey P. K. ( 2017 ). Secure cloud-based image tampering detection and localization using POB number system . ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) , 13 ( 3 ), 1 - 23 . Search in Google Scholar

[13] Goléa, N. E. H., & Melkemi, K. E. (2019). ROI-based fragile watermarking for medical image tamper detection. International Journal of High Performance Computing and Networking, 13(2), 199-210. Goléa N. E. H. Melkemi K. E. ( 2019 ). ROI-based fragile watermarking for medical image tamper detection . International Journal of High Performance Computing and Networking , 13 ( 2 ), 199 - 210 . Search in Google Scholar

[14] Soualmi, A., Alti, A., & Laouamer, L. (2022). An imperceptible watermarking scheme for medical image tamper detection. International Journal of Information Security and Privacy (IJISP), 16(1), 1-18. Soualmi A. Alti A. Laouamer L. ( 2022 ). An imperceptible watermarking scheme for medical image tamper detection . International Journal of Information Security and Privacy (IJISP) , 16 ( 1 ), 1 - 18 . Search in Google Scholar

[15] Wang, J., Wu, Z., Chen, J., Han, X., Shrivastava, A., Lim, S. N., & Jiang, Y. G. (2022). Objectformer for image manipulation detection and localization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2364-2373). Wang J. Wu Z. Chen J. Han X. Shrivastava A. Lim S. N. Jiang Y. G. ( 2022 ). Objectformer for image manipulation detection and localization . In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2364 - 2373 ). Search in Google Scholar

[16] Kosuru, S. D., Swain, G., Kumar, N., & Pradhan, A. (2022). Image tamper detection and correction using Merkle tree and remainder value differencing. Optik, 261, 169212. Kosuru S. D. Swain G. Kumar N. Pradhan A. ( 2022 ). Image tamper detection and correction using Merkle tree and remainder value differencing . Optik , 261 , 169212 . Search in Google Scholar

[17] Rhayma, H., Makhloufi, A., Hamam, H., & Hamida, A. B. (2021). Semi-fragile watermarking scheme based on perceptual hash function (PHF) for image tampering detection. Multimedia Tools and Applications, 80(17), 26813-26832. Rhayma H. Makhloufi A. Hamam H. Hamida A. B. ( 2021 ). Semi-fragile watermarking scheme based on perceptual hash function (PHF) for image tampering detection . Multimedia Tools and Applications , 80 ( 17 ), 26813 - 26832 . Search in Google Scholar

[18] Sahu, A. K., Hassaballah, M., Rao, R. S., & Suresh, G. (2023). Logistic-map based fragile image watermarking scheme for tamper detection and localization. Multimedia Tools and Applications, 82(16), 24069-24100. Sahu A. K. Hassaballah M. Rao R. S. Suresh G. ( 2023 ). Logistic-map based fragile image watermarking scheme for tamper detection and localization . Multimedia Tools and Applications , 82 ( 16 ), 24069 - 24100 . Search in Google Scholar

[19] Nirmal Jothi, J., & Letitia, S. (2020). Tampering detection using hybrid local and global features in wavelet-transformed space with digital images. Soft Computing, 24(7), 5427-5443. Nirmal Jothi J. Letitia S. ( 2020 ). Tampering detection using hybrid local and global features in wavelet-transformed space with digital images . Soft Computing , 24 ( 7 ), 5427 - 5443 . Search in Google Scholar

[20] Yusoff, N., & Alamro, L. (2019). Implementation of feature extraction algorithms for image tampering detection. International Journal of Advanced Computer Research, 9(43), 197-211. Yusoff N. Alamro L. ( 2019 ). Implementation of feature extraction algorithms for image tampering detection . International Journal of Advanced Computer Research , 9 ( 43 ), 197 - 211 . Search in Google Scholar

[21] Barani, M. J., Valandar, M. Y., & Ayubi, P. (2019). A new digital image tamper detection algorithm based on integer wavelet transform and secured by encrypted authentication sequence with 3D quantum map. Optik, 187, 205-222. Barani M. J. Valandar M. Y. Ayubi P. ( 2019 ). A new digital image tamper detection algorithm based on integer wavelet transform and secured by encrypted authentication sequence with 3D quantum map . Optik , 187 , 205 - 222 . Search in Google Scholar

[22] Sahu, A. K., Sahu, M., Patro, P., Sahu, G., & Nayak, S. R. (2023). Dual image-based reversible fragile watermarking scheme for tamper detection and localization. Pattern Analysis and Applications, 26(2), 571-590. Sahu A. K. Sahu M. Patro P. Sahu G. Nayak S. R. ( 2023 ). Dual image-based reversible fragile watermarking scheme for tamper detection and localization . Pattern Analysis and Applications , 26 ( 2 ), 571 - 590 . Search in Google Scholar

[23] Haghighi, B. B., Taherinia, A. H., & Harati, A. (2018). TRLH: Fragile and blind dual watermarking for image tamper detection and self-recovery based on lifting wavelet transform and halftoning technique. Journal of Visual Communication and Image Representation, 50, 49-64. Haghighi B. B. Taherinia A. H. Harati A. ( 2018 ). TRLH: Fragile and blind dual watermarking for image tamper detection and self-recovery based on lifting wavelet transform and halftoning technique . Journal of Visual Communication and Image Representation , 50 , 49 - 64 . Search in Google Scholar

[24] Alaa A. Abdullatif,Firas A. Abdullatif & Amna Al Safar. (2019). Multi -Focus Image Fusion Based on Stationary Wavelet Transform and PCA on YCBCR Color Space. Journal of Southwest Jiaotong University(5). Alaa A. Abdullatif Firas A. Abdullatif Amna Al Safar ( 2019 ). Multi -Focus Image Fusion Based on Stationary Wavelet Transform and PCA on YCBCR Color Space . Journal of Southwest Jiaotong University ( 5 ). Search in Google Scholar

[25] Wang Huanying,Yuan Zihan,Chen Siyu & Su Qingtang. (2023). Embedding color watermark image to color host image based on 2D-DCT.Optik. Wang Huanying Yuan Zihan Chen Siyu Su Qingtang ( 2023 ). Embedding color watermark image to color host image based on 2D-DCT.Optik . Search in Google Scholar

[26] Shahrokhi Marziye,Akoushideh Alireza & Shahbahrami Asadollah. (2022). Image Copy–Move Forgery Detection Using Combination of Scale-Invariant Feature Transform and Local Binary Pattern Features. International Journal of Image and Graphics(05). Shahrokhi Marziye Akoushideh Alireza Shahbahrami Asadollah ( 2022 ). Image Copy–Move Forgery Detection Using Combination of Scale-Invariant Feature Transform and Local Binary Pattern Features . International Journal of Image and Graphics ( 05 ). Search in Google Scholar

[27] Amani Alahmadi,Muhammad Hussain,Hatim Aboalsamh,Ghulam Muhammad, George Bebis & Hassan Mathkour. (2017). Passive detection of image forgery using DCT and local binary pattern. Signal, Image and Video Processing(1),81-88. Amani Alahmadi Muhammad Hussain Hatim Aboalsamh Ghulam Muhammad George Bebis Hassan Mathkour ( 2017 ). Passive detection of image forgery using DCT and local binary pattern . Signal, Image and Video Processing ( 1 ), 81 - 88 . Search in Google Scholar

[28] Diaz M. P. & Jiju Julie Emerald M.. (2022). A Hybrid Moth-Flame Optimization Technique for Feature Selection in Brain Image Classification and Image Denoising by Improved Log Gabor Filter. International Journal of Computer Vision and Image Processing (IJCVIP)(1),1-20. Diaz M. P. Jiju Julie Emerald M. ( 2022 ). A Hybrid Moth-Flame Optimization Technique for Feature Selection in Brain Image Classification and Image Denoising by Improved Log Gabor Filter . International Journal of Computer Vision and Image Processing (IJCVIP) ( 1 ), 1 - 20 . Search in Google Scholar