Skip to main content

Advertisement

SpringerLink
Log in

A Study on Content Tampering in Multimedia Watermarking

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Technological progresses offer more occasions for tampering outbreaks. Lithography services at affordable prices, in aggregation with open software tools to influence changes in digital spaces, invigorated amateurs to oblige to fabricating and imitating. Tampering has reached a level of cleverness that leaves negligible trace with the pace of progress happening with editing technology, luring the next generation. However tampering with technology violates intellectual property rights that can be treated to cost dearly, including severe retributions. At the same time, accountability for evading, aiding the evasion of technology, and restricting access that could impede the infringement of the content are new protection activities, which were not present during the pre-digital age. Digitalization shrinks the cost of content development, nevertheless availability of pirated content is also on the rise due to ease of copy, transform and distribution. Surveillance is a big dataset, with arrangements from various sources at diverse scenarios that are critical to events, therefore, susceptible errors such as defocusing, occlusion and displacement. The forensic study is thus correlated, for prediction using multi-task joint model through convolutional neural network (CNN), as they are open to access in metadata, also its alteration through ease of EXIF tools provide innumerable opportunities to tamper contents, thus tough to identify, except after severe inquiries. In this study, we present a brief overview of recent status with respect to the content tampering using several advanced tools.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data availability

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

References

  1. Mashelkar RA. Indian science, technology, and society: the changing landscape. Technol Soc. 2008;30(3–4):299–308.

    Article  Google Scholar 

  2. William JC. USN Eleventh Chairman of the Joint Chiefs of Staff, Admiral William J. Crowe, Jr., Joint History Office, Office of the Chairman of the Joint Chiefs of Staff Washington, DC, 2013, pp 1–155. https://www.jcs.mil/Portals/36/Documents/History/Monographs/crrowe.pdf

  3. Pew Research Center. The future of well being in a tech-saturated world. In: Pew Research Center. Cham: Springer; 2018. p. 1–273.

    Google Scholar 

  4. Ines VBI, Reding A, Edwards C, Gribbon L. Radicalisation in the digital era The use of the internet in 15 cases of terrorism and extremism, Rand Europe, 2013, pp. 1–76.

  5. ICMR. Intellectual Property Rights & Technology Transfer, Handbook on IPR & Technology Transfer, Indian Council of Medical Research, 2017, p. 85. https://main.icmr.nic.in/sites/default/files/Books/Intellectual_property_rights_pdf.

  6. Parashar N, Tiwari N, Dubey D. A survey of digital image tampering techniques. Int J Signal Process Image Process Pattern Recognit. 2015;8(10):91–6.

    Google Scholar 

  7. Cui Y, Lei Y. CV-SIFT algorithm for image detection. In: 2nd international symposium on computer, communication, control and automation. London: Atlantis Press; 2013. p. 285–7.

    Google Scholar 

  8. Waldfogel J. How digitization has created a golden age of music, movies, books, and television. J Econ Perspect. 2017;31(3):195–214.

    Article  Google Scholar 

  9. Gangwar DP, Pathania A. Authentication of digital image using exif metadata and decoding properties. Int J Sci Res Comput Sci Eng Inf Technol. 2018;3(8):335–41.

    Google Scholar 

  10. Patil RD, Metkar S. Fragile video watermarking for tampering detection and localization. In: 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, 2015, pp. 1661–6.

  11. Chatila R, Havens JC. The IEEE global initiative on ethics of autonomous and intelligent systems. In: Robotics and well-being. Cham: Springer; 2019. p. 11–6.

    Chapter  Google Scholar 

  12. Kim J. Protecting metadata of access indicator and region of interests for image files. Secur Commun Netw. 2020. https://doi.org/10.1155/2020/4836109.

    Article  Google Scholar 

  13. Stanton JM, Weiss EM. Electronic monitoring in their own words: an exploratory study of employees’ experiences with new types of surveillance. Comput Hum Behav. 2000;16(4):423–40.

    Article  Google Scholar 

  14. Karimaa A. Mobile and wireless access in video surveillance system. In: International conference on digital information and communication technology and its applications. Berlin: Springer; 2011. p. 131–8.

    Google Scholar 

  15. Olson JS, Olson GM, Meader DK. What mix of video and audio is useful for small groups doing remote real-time design work? In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 1995, pp. 362–368.

  16. Viega J, McGraw GR. Building secure software: how to avoid security problems the right way, portable documents. Cham: Pearson Education; 2001.

    Google Scholar 

  17. Gil-Jiménez P, López-Sastre R, Siegmann P, Acevedo-Rodríguez J, Maldonado-Bascón S. Automatic control of video surveillance camera sabotage. In: International work-conference on the interplay between natural and artificial computation. Berlin: Springer; 2007. p. 222–31.

    Google Scholar 

  18. Kryjak T, Komorkiewicz M, Gorgon M. FPGA implementation of camera tamper detection in real-time. In: Proceedings of the 2012 Conference on Design and Architectures for Signal and Image Processing, IEEE, 2012, pp. 1–8.

  19. Saglam A, Temizel A. Real-time adaptive camera tamper detection for video surveillance. In: 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, 2009, (pp. 430–5.

  20. Wang YK, Fan CT, Cheng KY, Deng PS. Real-time camera anomaly detection for real-world video surveillance. Int Conf Machine Learn Cybern. 2011;4:1520–5.

    Google Scholar 

  21. Bhatele KR, Shrivastava H, Kumari N. The role of artificial intelligence in cyber security. In: Global IGI, editor. Countering cyber attacks and preserving the integrity and availability of critical systems. IGI Global; 2019. p. 170–92.

    Chapter  Google Scholar 

  22. Hb BG, Poornachandran P, Kp S. Deep-net: Deep neural network for cyber security use cases. 2018. arXiv preprint arXiv:1812.03519.

  23. Chen Y. Improving market performance in the digital economy. China Econ Rev. 2020;62: 101482.

    Article  Google Scholar 

  24. An FP, Liu JE, Bai L. Object recognition algorithm based on optimized nonlinear activation function-global convolutional neural network. Vis Comput. 2022;38(2):541–53.

    Article  Google Scholar 

  25. Ronaldo R, Freitas PG, Mylène-Farias CQ. Detecting tampering in audio-visual content using QIM watermarking. Inf Sci. 2016;328:127–43.

    Article  Google Scholar 

  26. Manjunatha S, Patil MM.. Deep learning-based technique for image tamper detection. In: 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), IEEE, 2021, pp. 1278–85.

  27. Zhang Y, Goh J, Win LL, Thing VL. Image region forgery detection: a deep learning approach. SG-CRC. 2016;2016:1–11.

    Google Scholar 

  28. Barad ZJ, Goswami MM. Image forgery detection using deep learning: a survey. In: 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), IEEE, 2020, pp 571–6.

  29. Doegar A, Dutta M, Kumar G. Image forgery detection using google net and random forest machine learning algorithm. J Univ Shanghai Sci Technol. 2020;2020:1271–8.

    Google Scholar 

  30. Islam MM, Karmakar G, Kamruzzaman J, Murshed M. A robust forgery detection method for copy–move and splicing attacks in images. Electronics. 2020;9(9):1500.

    Article  Google Scholar 

  31. Yao Y, Shi Y, Weng S, Guan B. Deep learning for detection of object-based forgery in advanced video. Symmetry. 2017;10(1):3.

    Article  Google Scholar 

  32. Meena KB, Tyagi V. A deep learning based method for image splicing detection. J Phys. 2021;1714(1):012038.

    Google Scholar 

  33. Sahu M, Padhy N, Gantayat SS, Sahu AK. Local binary pattern-based reversible data hiding. CAAI Trans Intell Technol. 2022;7(4):695–709.

    Article  Google Scholar 

  34. Sahu AK, Hassaballah M, Rao RS, et al. Logistic-map based fragile image watermarking scheme for tamper detection and localization. Multimed Tools Appl. 2022. https://doi.org/10.1007/s11042-022-13630-4.

    Article  Google Scholar 

  35. Srivastava P, Kumar M, Deep V, Sharma P. A technique to detect copy-move forgery using enhanced SURF. Int J Eng Adv Technol. 2019;8:676–80.

    Article  Google Scholar 

  36. Akhtar S, Ghosal D, Ekbal A, Bhattacharyya P, Kurohashi S. All-in-one: Emotion, sentiment and intensity prediction using a multi-task ensemble framework. IEEE Trans Affect Comput. 2019;13:285–7.

    Article  Google Scholar 

  37. Umachandran K. Human-Machine interface with deep neural network, MGM University International Workshop (One day) 7th May 2022, 2022, pp. 1–28.

  38. Abdulnabi AH, Wang G, Lu J, Jia K. Multi-task CNN model for attribute prediction. IEEE Trans Multimedia. 2015;17(11):1949–59.

    Article  Google Scholar 

  39. Li R, Tian B, Li Y, Qu Y. Information security evaluation based on artificial neural network. Int J Perform Eng. 2019;15(11):2908.

    Article  Google Scholar 

  40. Janabi AH, Kanakis T, Johnson M. Convolutional neural network based algorithm for early warning proactive system security in software defined networks. IEEE Access. 2022;10:14301–10.

    Article  Google Scholar 

  41. Sahu AK, Gutub A. Improving grayscale steganography to protect personal information disclosure within hotel services. Multimedia Tools Appl. 2022;81:30663–83.

    Article  Google Scholar 

  42. Zhang A, Lipton ZC, Li M, Smola AJ. Dive into deep learning. 2021. arXiv preprint arXiv:2106.11342.

  43. Wilding D, Fray P, Molitorisz S, McKewon E. The impact of digital platforms on news and journalistic content. University of Technology Sydney; 2018. p. 1–175.

    Google Scholar 

  44. Corbion AP, Hosein G, Fisher T, Geraghty E, Quintanilla J, Marelli M, Pelucchi S. The humanitarian metadata problem:‘doing no harm’in the digital era. Privacy International and ICRC. 2018. https://privacyinternational.org

  45. Iqbal S, Kiah MLM, Dhaghighi B, Hussain M, Khan S, Khan MK, Choo KKR. On cloud security attacks: a taxonomy and intrusion detection and prevention as a service. J Netw Comput Appl. 2016;74:98–120.

    Article  Google Scholar 

  46. Yaacoub JA, Salman O, Noura HN, Kaaniche N, Chehab A, Malli M. Cyber-physical systems security: Limitations, issues and future trends. Microprocessors Microsyst. 2020;77:103201. https://doi.org/10.1016/j.micpro.2020.103201.

    Article  Google Scholar 

  47. Wu C, Buyya R, Ramamohanarao K. Big data analytics= machine learning+ cloud computing. 2016. arXiv preprint arXiv:1601.03115.

  48. Ghosh P, Morariu V, Larry Davis BCI. Detection of metadata tampering through discrepancy between image content and metadata using multi-task deep learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2017, pp. 60–8.

  49. Tsesmelis T, Christensen L, Fihl P, Moeslund TB. Tamper detection for active surveillance systems. 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, 2013, pp. 57–62.

  50. Kursawe K, Sadeghi AR, Schellekens D, Skoric B, Tuyls P. Reconfigurable physical unclonable functions-enabling technology for tamper-resistant storage. In: 2009 IEEE International Workshop on Hardware-Oriented Security and Trust, IEEE, pp. 22–9.

  51. SSRC. Media piracy in emerging economies. Social Science Research Council; 2011.

    Google Scholar 

  52. Tyagi S, Yadav D. A detailed analysis of image and video forgery detection techniques. Vis Comput. 2022. https://doi.org/10.1007/s00371-021-02347-4.

    Article  Google Scholar 

  53. Shevchenko N, Chick TA, O’Riordan P, Scanlon TP, Woody C. Threat modeling: a summary of available methods. Carnegie Mellon University Software Engineering Institute Pittsburgh United States; 2018.

    Google Scholar 

  54. Suresh G, Narla VL, Gangwar DP, Sahu AK. False-positive-free SVD based audio watermarking with integer wavelet transform. Circuits Syst Signal Process. 2022. https://doi.org/10.1007/s00034-022-02023-5.

    Article  Google Scholar 

  55. Qian Z, Gu Y, Hong W. An image tampering detection algorithm of qualification certificate based on CNN and SVM. Acad J Comput Inf Sci. 2021. https://doi.org/10.25236/AJCIS.2021.040705.

    Article  Google Scholar 

  56. Sahu AK. A logistic map based blind and fragile watermarking for tamper detection and localization in images. J Ambient Intell Humaniz Comput. 2022;13(8):3869–81.

    Article  Google Scholar 

Download references

Funding

We declare this work is an independent work and no financial assistance has been received for the work.

Author information

Authors and Affiliations

  1. Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amaravati, Andhra Pradesh, India

    Aditya Kumar Sahu

  2. General Manager (Organization Development), Nelcast Ltd., Chennai, India

    Krishnan Umachandran

  3. Department of Electronics and Telecommunication Engineering, Dr. D.Y. Patil Institute of Technology, Pimpri, Pune, India

    Vaishali D. Biradar

  4. Computer Science, Imo State University, Owerri, Nigeria

    Olebara Comfort

  5. Department of Information Technology, Bannari Amman Institute of Technology, Tamilnadu, India

    V. Sri Vigna Hema

  6. North West University Mafikeng, Mahikeng, South Africa

    Frank Odimegwu

  7. SCIS, University of Hyderabad, Central University P.O., Hyderabad, India

    Saifullah M. A

Authors
  1. Aditya Kumar Sahu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Krishnan Umachandran
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vaishali D. Biradar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Olebara Comfort
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. V. Sri Vigna Hema
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Frank Odimegwu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Saifullah M. A
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Thanks to the international collaborations among the authors to complete this work, the paper has been contributed equally by all the authors.

Corresponding author

Correspondence to Aditya Kumar Sahu.

Ethics declarations

Conflict of Interest

We declare we have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sahu, A.K., Umachandran, K., Biradar, V.D. et al. A Study on Content Tampering in Multimedia Watermarking. SN COMPUT. SCI. 4, 222 (2023). https://doi.org/10.1007/s42979-022-01657-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-022-01657-1

Keywords

Search

Navigation