REFERENCES

1. Birajdar GK, Mankar VH. Digital image forgery detection using passive techniques: a survey. Digital Investigation 2013;10:226-45.

2. Qureshi MA, Deriche M. A bibliography of pixel-based blind image forgery detection techniques. Signal Processing: Image Communication 2015;39:46-74.

3. Ke Y, Shan Q, Qin F, Min W, Guo J. Detection of seam carved image based on additional seam carving behavior. IJSIP 2016;9:167-78.

4. Avidan S, Shamir A. Seam carving for content-aware image resizing. ACM Trans Graph 2007;26:10.

5. Sarkar A, Nataraj L, Manjunath BS. Detection of seam carving and localization of seam insertions in digital images, Proc. 11th ACM Workshop on Multimedia and Security (MM&Sec'09), NY, USA, pp. 107-116, 2009.

6. Yin T, Yang G, Li L, Zhang D, Sun X. Detecting seam carving based image resizing using local binary patterns. Computers & Security 2015;55:130-41.

7. Liu Q. An approach to detecting JPEG down-recompression and seam carving forgery under recompression anti-forensics. Pattern Recognition 2017;65:35-46.

8. Lu M, Niu S. Detection of image seam carving using a novel pattern. Comput Intell Neurosci 2019;2019:9492358.

9. SSeam carving based image resizing detection using hybrid features. Teh vjesn 2017;24.

10. Jassim S, Asaad A. Automatic detection of image morphing by topology-based analysis. In 2018 26th European Signal Processing Conference (EUSIPCO), pages 1007–1011. IEEE, 2018.

11. Neubert T. Face Morphing Detection: An Approach Based on Image Degradation Analysis. In: Kraetzer C, Shi Y, Dittmann J, Kim HJ, editors. Digital Forensics and Watermarking. Cham: Springer International Publishing; 2017. pp. 93-106.

12. Popescu A, Farid H. Exposing digital forgeries by detecting traces of resampling. IEEE Trans Signal Process 2005;53:758-67.

13. Ryu S, Lee H. Estimation of linear transformation by analyzing the periodicity of interpolation. Pattern Recognition Letters 2014;36:89-99.

14. Guillemot C, Le Meur O. Image inpainting : overview and recent advances. IEEE Signal Process Mag 2014;31:127-44.

15. Salloum R, Ren Y, Jay Kuo C. Image Splicing Localization using a Multi-task Fully Convolutional Network (MFCN). Journal of Visual Communication and Image Representation 2018;51:201-9.

16. Cozzolino D, Poggi G, Verdoliva L. Efficient Dense-Field Copy-Move Forgery Detection. IEEE Trans Inform Forensic Secur 2015;10:2284-97.

17. Jian Li, Xiaolong Li, Bin Yang, Xingming Sun. Segmentation-based image copy-move forgery detection scheme. IEEE Trans Inform Forensic Secur 2015;10:507-18.

18. Gong Q, Shan Q, Ke Y, Guo J. Detecting the location of seam and recovering image for seam inserted image. JCM 2018;18:499-509.

19. Li Y, Xia M, Liu X, Yang G. Identification of various image retargeting techniques using hybrid features. Journal of Information Security and Applications 2020;51:102459.

20. Liang Z, Yang G, Ding X, Li L. An efficient forgery detection algorithm for object removal by exemplar-based image inpainting. Journal of Visual Communication and Image Representation 2015;30:75-85.

21. Wu Q, Sun SJ, Zhu W, Li GH, Tu D. Detection of digital doctoring in exemplar-based inpainted images. In Machine Learning and Cybernetics, 2008 International Conference on. 2008;3: 1222-6.

22. Farid H. Exposing Digital Forgeries From JPEG Ghosts. IEEE Trans Inform Forensic Secur 2009;4:154-60.

23. Luo W, Huang J, Qiu G. JPEG Error Analysis and Its Applications to Digital Image Forensics. IEEE Trans Inform Forensic Secur 2010;5:480-91.

24. Bayar B, Stamm MC. A deep learning approach to universal image manipulation detection using a new convolutional layer. In Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security. 2016: 5-10.

25. Rao Y, Ni JQ. A deep learning approach to detection of splicing and copy-move forgeries in images. In Information Forensics and Security (WIFS), International Workshop on. 2016: 1–6.

26. Liu Q, Chen Z, Liu HC. A comparison study to detect COVID-19 X-ray images with SOTA deep learning models. Proceedings of the 1st Workshop on Healthcare AI and COVID-19. ICML 2022;184:146-153.

27. Shi YQ, Chen C, Chen W. A Markov Process Based Approach to Effective Attacking JPEG Steganography. In: Camenisch JL, Collberg CS, Johnson NF, Sallee P, editors. Information Hiding. Berlin: Springer Berlin Heidelberg; 2007. pp. 249-64.

28. Fillion C, Sharma G. Detecting content adaptive scaling of images for forensic applications. Media Forensics and Security, SPIE Proc., p. 75410, 2010.

29. Chang W, Shih TK, Hsu H. Detection of seam carving in JPEG images. in: Proceedings of the 2013 International Joint Conference on Awareness Science and Technology and Ubi-media Computing, pp 632-638, 2013.

30. Wattanachote K, Shih TK, Chang W, Chang H. Tamper Detection of JPEG Image Due to Seam Modifications. IEEE Trans Inform Forensic Secur 2015;10:2477-91.

31. Fridrich J, Kodovsky J. Rich models for steganalysis of digital images. IEEE Trans Inform Forensic Secur 2012;7:868-82.

32. Liu Q. Exposing seam carving forgery under recompression attacks by hybrid large feature mining. 23rd Int. Conf. on Pattern Recog. (ICPR), pp. 1036-1041, 2016.

33. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016: 770-778.

34. Nam SH, Park J, Kim D, Yu IJ, Kim TY, Lee HK. Two stream network for detecting double compression of h. 264 videos, in 2019 IEEE International Conference on Image Processing (ICIP). IEEE, 2019, pp. 111–115.

35. Gallucci M. At last, wave energy tech plugs into the grid - [News]. IEEE Spectr 2019;56:8-9.

36. He P, Jiang X, Sun T, Wang S, Li B, Dong Y. Frame-wise detection of relocated I-frames in double compressed H.264 videos based on convolutional neural network. Journal of Visual Communication and Image Representation 2017;48:149-58.

37. Ye J, Shi Y, Xu G, Shi Y. A Convolutional Neural Network Based Seam Carving Detection Scheme for Uncompressed Digital Images. In: Yoo CD, Shi Y, Kim HJ, Piva A, Kim G, editors. Digital Forensics and Watermarking. Cham: Springer International Publishing; 2019. pp. 3-13.

38. Ono Y, Trulls E, Fua PV, Yi KM. (2018). LF-Net: Learning Local Features from Images. ArXiv, abs/1805.09662.

39. Boroumand M, Chen M, Fridrich J. Deep Residual Network for Steganalysis of Digital Images. IEEE Trans Inform Forensic Secur 2019;14:1181-93.

40. Liu Q. An Improved Approach to Exposing JPEG Seam Carving Under Recompression. IEEE Trans Circuits Syst Video Technol 2019;29:1907-18.

41. Diallo B, Urruty T, Bourdon P, Fernandez-maloigne C. Robust forgery detection for compressed images using CNN supervision. Forensic Science International: Reports 2020;2:100112.

42. Majumder MTH, Islam AAA. A tale of a deep learning approach to image forgery detection, in 2018 5th International Conference on Networking, Systems and Security (NSysS). IEEE, 2018, pp. 1–9.

43. Yao H, Xu M, Qiao T, Wu Y, Zheng N. Image Forgery Detection and Localization via a Reliability Fusion Map. Sensors (Basel) 2020;20:6668.

44. Du M, Pentyala S, Li Y, Hu X. Towards generalizable deepfake detection with locality-aware autoencoder, in Proceedings of the 29thACM International Conference on Information & Knowledge Management, 2020, pp. 325–334.

45. Thakur A, Jindal N. Machine learning based saliency algorithm for image forgery classification and localization, in 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC) IEEE, 2018, pp. 451–456.

46. Muhammad G, Al-hammadi MH, Hussain M, Bebis G. Image forgery detection using steerable pyramid transform and local binary pattern. Machine Vision and Applications 2014;25:985-95.

47. Warif NBA, Idris MYI, Wahab AWA, Ismail NN, Salleh R. A comprehensive evaluation procedure for copy-move forgery detection methods: results from a systematic review. Multimed Tools Appl 2022;81:15171-203.

48. Le-tien T, Phan-xuan H, Nguyen-chinh T, Do-tieu T; The authors are with the Department of Electrical and Electronics Engineering, University of Technology, National University of Ho Chi Minh city, Vietnam. Image forgery detection: a low computational-cost and effective data-driven model. IJMLC 2019;9: 181-8.

49. Han J, Gevers T. MMD Based Discriminative Learning for Face Forgery Detection. In: Ishikawa H, Liu C, Pajdla T, Shi J, editors. Computer Vision - ACCV 2020. Cham: Springer International Publishing; 2021. pp. 121-36.

50. Aneja S, Nießner M. Generalized zero and few-shot transfer for facial forgery detection, arXiv preprint arXiv: 2006.11863, 2020.

51. Bourouis S, Mashrgy MA, Bouguila N. Bayesian learning of finite generalized inverted Dirichlet mixtures: application to object classification and forgery detection. Expert Systems with Applications 2014;41:2329-36.

52. Cao S, Zou Q, Mao X, Wang Z. Metric learning for anti-compression facial forgery detection, arXiv preprint arXiv: 2103.08397, 2021.

53. Srivastava V, Yadav SK. Frequency based edge-texture feature using Otsu's based enhanced local ternary pattern technique for digital image splicing detection. Bulletin EEI 2021;10:3147-55.

54. Zhang R, Ni J. A dense u-net with cross-layer intersection for detection and localization of image forgery, in Proc. 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2982–2986.

55. Bottou L. On-line Learning and Stochastic Approximations. In: Saad D, editor. On-Line Learning in Neural Networks. Cambridge University Press; 2010. pp. 9-42.

56. Kingma D, Ba J. (2014). Adam: A Method for Stochastic Optimization. arXiv: 1412.6980.

57. Loshchilov I, Hutter F. (2019). Decoupled Weight Decay Regularization. ICLR (Poster) 2019, arXiv: 1711.05101v3.

58. "Torch. optim¶, " torch. optim - PyTorch 1.11.0 documentation. [Online]. Available from: https://pytorch.org/docs/stable/optim.html [Last accessed on 9 Aug 2022].

59. Tan M, Le QV. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. ArXiv, abs/1905.11946.

60. Nam S, Ahn W, Yu I, Kwon M, Son M, Lee H. Deep Convolutional Neural Network for Identifying Seam-Carving Forgery. IEEE Trans Circuits Syst Video Technol 2021;31:3308-26.

61. Lu M, Niu SZ. (2019), Detection of Image Seam Carving Using a Novel Pattern, Computational Intelligence and Neuroscience, vol. 2019, Article ID 9492358, 15 pages, 2019.

62. Senturk ZK, Akgun D. Seam carving based image resizing detection using hybrid features. Tehnicki Vjesnik 2017;24:1825-1832.

63. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In NIPS, pp. 1106–1114, 2012.

64. Chollet F. Xception: Deep learning with depthwise separable convolutions, arXiv preprint, pp. 1610–02 357, 2017.