Abstract

In recent times, the popularity of digital photographs has increased due to their ability to convey more information than conventional image and text content. However, their easy accessibility has made ensuring their security a major concern. Therefore, serious problems can be quite challenging to minimize when testing and evaluating the validity of a study problem and identifying malevolent intruders. To address these challenges, various digital image forensics techniques have been proposed by numerous researchers to identify the forensics of an image and verify its content. The passive method and the active approach are the two most used techniques in digital forensics. In this paper, a digital image security forensics technique with different machine learning and deep learning classifiers was reviewed to demonstrate the effectiveness of the approach. The use of these techniques was explored to enhance the accuracy of digital image forensics.

Keyword

Digital photographs, Image forensics, Security, Machine learning.

Cite this article

Singh C, Hashmi MA

Refference

[1][1]Zhang Y, Yan Y, Feng G. Feature compensation network based on non-uniform quantization of channels for digital image global manipulation forensics. Signal Processing: Image Communication. 2022; 107.

[2][2]El-Bendary MA, Faragallah OS, Nassar SS. An efficient hidden marking approach for forensic and contents verification of digital images. Multimedia Tools and Applications. 2023:1-32.

[3][3]Akbari Y, Al-maadeed S, Elharrouss O, Khelifi F, Lawgaly A, Bouridane A. Digital forensic analysis for source video identification: a survey. Forensic Science International: Digital Investigation. 2022.

[4][4]Liu K, Li J, Hussain Bukhari SS. Overview of image inpainting and forensic technology. Security and Communication Networks. 2022; 2022:1-27.

[5][5]Liu C. A proposal of digital image steganography and forensics based on the structure of file storage. In proceedings of the 11th international conference on computer engineering and networks 2022 (pp. 731-40). Springer Singapore.

[6][6]Akbari Y, Al-Maadeed S, Al-Maadeed N, Al-Ali A, Khelifi F, Lawgaly A. A new forensic video database for source smartphone identification: description and analysis. IEEE Access. 2022; 10:20080-91.

[7][7]Muhammad NA, Fatima R. Role of image processing in digital forensics and cybercrime detection. International Journal of Computational and Innovative Sciences. 2022; 1(1):1-4.

[8][8]Bernacki J, Scherer R. Digital forensics: a fast algorithm for a digital sensor identification. Journal of Information and Telecommunication. 2022; 6(4):399-419.

[9][9]Bansal D, Passi A. Image forgery detection and localization using block based and key-point based feature matching forensic investigation. Wireless Personal Communications. 2022:1-7.

[10][10]Khan AA, Shaikh AA, Laghari AA, Dootio MA, Rind MM, Awan SA. Digital forensics and cyber forensics investigation: security challenges, limitations, open issues, and future direction. International Journal of Electronic Security and Digital Forensics. 2022; 14(2):124-50.

[11][11]Sasubilli SM, Dubey AK, Kumar A. A computational and analytical approach for cloud computing security with user data management. In international conference on advances in computing and communication engineering 2020 (pp. 1-5). IEEE.

[12][12]Arava K, Paritala C, Shariff V, Praveen SP, Madhuri A. A generalized model for identifying fake digital images through the application of deep learning. In 3rd international conference on electronics and sustainable communication systems 2022 (pp. 1144-7). IEEE.

[13][13]Saleem M, Warsi MR, Islam S. Secure information processing for multimedia forensics using zero-trust security model for large scale data analytics in SaaS cloud computing environment. Journal of Information Security and Applications. 2023.

[14][14]Shahbazi Z, Byun YC. NLP-based digital forensic analysis for online social network based on system security. International Journal of Environmental Research and Public Health. 2022; 19(12).

[15][15]Zhu N, Liu Z. Recaptured image forensics based on local ternary count of high order prediction error. Signal Processing: Image Communication. 2022.

[16][16]Patil SS, Patidar K, Saxena G, Sharma N. A study and analysis of image security algorithms and the current challenges. ACCENTS Transactions on Image Processing and Computer Vision. 2021; 7(22): 1-6.

[17][17]Alyahya H, Ismail MM, Al-Salman A. Deep ensemble neural networks for recognizing isolated Arabic handwritten characters. ACCENTS Transactions on Image Processing and Computer Vision. 2020; 6(21):68-79.

[18][18]Das D, Bose P, Ruaro N, Kruegel C, Vigna G. Understanding security issues in the NFT ecosystem. In proceedings of the ACM SIGSAC conference on computer and communications security 2022 (pp. 667-81).

[19][19]Rustad S, Syukur A, Andono PN. Inverted LSB image steganography using adaptive pattern to improve imperceptibility. Journal of King Saud University-Computer and Information Sciences. 2022; 34(6):3559-68.

[20][20]Prasanalakshmi B, Murugan K, Srinivasan K, Shridevi S, Shamsudheen S, Hu YC. Improved authentication and computation of medical data transmission in the secure IoT using hyperelliptic curve cryptography. The Journal of Supercomputing. 2022; 78(1):361-78.

[21][21]Lin X, Li JH, Wang SL, Cheng F, Huang XS. Recent advances in passive digital image security forensics: a brief review. Engineering. 2018; 4(1):29-39.

[22][22]Shuja SM, Khan RF, Shah MA, Khattak HA, Abbass A, Khan SU. On efficiency of scrambled image forensics service using support vector machine. In Services–SERVICES 2019: 15th world congress, held as part of the services conference federation, SCF 2019, San Diego, CA, USA, , Proceedings 2019 (pp. 16-30). Springer International Publishing.

[23][23]Boato G, Dang-Nguyen DT, De Natale FG. Morphological filter detector for image forensics applications. IEEE Access. 2020; 8:13549-60.

[24][24]Aditya K, Grzonkowski S, Lekhac NA. Enabling trust in deep learning models: a digital forensics case study. In international conference on trust, security and privacy in computing and communications/12th international conference on big data science and engineering (TrustCom/BigDataSE) 2018 (pp. 1250-5). IEEE.

[25][25]Kakkad V, Patel M, Shah M. Biometric authentication and image encryption for image security in cloud framework. Multiscale and Multidisciplinary Modeling, Experiments and Design. 2019; 2:233-48.

[26][26]Karthika P, Vidhya SP. Image security performance analysis for SVM and ANN classification techniques. International Journal of Recent Technology and Engineering. 2019; 8(4S2):436-42.

[27][27]Cha S, Kang U, Choi E. The image forensics analysis of jpeg image manipulation (lightning talk). In international conference on software security and assurance (ICSSA) 2018 (pp. 82-5). IEEE.

[28][28]Elkandoz MT, Alexan W, Hussein HH. Double-layer image security scheme with aggregated mathematical sequences. In international conference on advanced communication technologies and networking (CommNet) 2019 (pp. 1-7). IEEE.

[29][29]Karthika P, Babu RG, Jayaram K. Biometric based on steganography image security in wireless sensor networks. Procedia Computer Science. 2020; 167:1291-9.

[30][30]Roy A, Dixit R, Naskar R, Chakraborty RS. Digital image forensics. Singapore: Springer; 2020.

[31][31]Babu RG, Karthika P, Elangovan K. Performance analysis for image security using SVM and ANN classification techniques. In 3rd international conference on electronics, communication and aerospace technology 2019 (pp. 460-5). IEEE.

[32][32]Shankar K, Lakshmanaprabu SK. Optimal key based homomorphic encryption for color image security aid of ant lion optimization algorithm. International Journal of Engineering & Technology. 2018; 7(9):22-7.

[33][33]Susanto A, Rachmawanto EH, Mulyono IU, Sari CA, Sarker MK, Sazal MR. Triple layer image security using bit-shift, chaos, and stream encryption. Bulletin of Electrical Engineering and Informatics. 2020; 9(3):980-7.

[34][34]Hasan MK, Islam S, Sulaiman R, Khan S, Hashim AH, Habib S, et al. Lightweight encryption technique to enhance medical image security on internet of medical things applications. IEEE Access. 2021; 9:47731-42.

[35][35]Arora H, Soni GK, Kushwaha RK, Prasoon P. Digital image security based on the hybrid model of image hiding and encryption. In 6th international conference on communication and electronics systems 2021 (pp. 1153-7). IEEE.

[36][36]Kumar M, Kumar S, Nagar H. Comparative analysis of different steganography technique for image or data security. International Journal of Advanced Science & Technology. 2020; 29(4): 11246-53.

[37][37]Barni M, Kallas K, Nowroozi E, Tondi B. On the transferability of adversarial examples against CNN-based image forensics. In international conference on acoustics, speech and signal processing (ICASSP) 2019 (pp. 8286-90). IEEE.

[38][38]Sun Y, Shen X, Liu C, Zhao Y. Recaptured image forensics algorithm based on image texture feature. International Journal of Pattern Recognition and Artificial Intelligence. 2020; 34(03).

[39][39]Yang P, Baracchi D, Ni R, Zhao Y, Argenti F, Piva A. A survey of deep learning-based source image forensics. Journal of Imaging. 2020; 6(3):1-24.

[40][40]Chen Y, Kang X, Wang ZJ, Zhang Q. Densely connected convolutional neural network for multi-purpose image forensics under anti-forensic attacks. In proceedings of the 6th ACM workshop on information hiding and multimedia security 2018 (pp. 91-6).

[41][41]Chen Q, Liao Q, Jiang ZL, Fang J, Yiu S, Xi G, et al. File fragment classification using grayscale image conversion and deep learning in digital forensics. In security and privacy workshops (SPW) 2018 (pp. 140-7). IEEE.

[42][42]Ferreira WD, Ferreira CB, da Cruz Júnior G, Soares F. A review of digital image forensics. Computers & Electrical Engineering. 2020; 85:106685.