Techniques for Video Authenticity Analysis Using the Localization Tampering Method to Support Forensic CCTV Investigations
Ririn Anggraini(1*); Yudi Prayudi(2);
(1) Universitas Islam Indonesia Yogyakarta
(2) Universitas Islam Indonesia Yogyakarta
(*) Corresponding Author
AbstractClosed Circuit Television (CCTV) is frequently utilized as legal evidence in judical proceedings. However, the authenticity of CCTV footage is often contested, requiring forensic analysis to verify its reliability as digital evidence. This study aimed to assess the authenticity of video footage using the Localization Tampering method. To simulate manipulation, various manipulation techniques, such as zooming, cropping, converting to grayscale, deleting frames, and rotating video sections, were applied. The Localization Tampering method was then used to detect manipulated areas by analyzing individual frames, calculating their histograms, and interpreting the histogram graph result. The findings demonstrated the method's ability to accurately identify the location and duration of manipulated frames. This offered a valuable tool to support forensic investigations of CCTV footage. Furthermore, this study highlights the challenges in detecting manipulation in low-quality videos, which required more sophisticated remediation techniques. Despite these challenges, the Localization Tampering method demonstrated consistent reliability in preserving the integrity of video footage, making it a practical solution for verifying digital evidence in a legal context. Overall, this study provides an effective approach to ensure that manipulated videos can be identified and corrected, contributing to a more robust CCTV forensics process and maintaining the credibility as evidence in a crime case. KeywordsCCTV; Digital Forensics; Evidence; Histogram; Localization Tampering.
|
Full Text:PDF |
Article MetricsAbstract view: 34 timesPDF view: 8 times |
Digital Object Identifierhttps://doi.org/10.33096/ilkom.v16i3.2378.318-329 |
Cite |
References
M. Arsyad, E. I. Fitria, M. R. Farhan, and R. Maulana, "Decision Making System for Selecting the Best CCTV Brand Using the Simple Additive Weighting (SAW) Method," FUSION Journal, vol. 3, no. 02, 2023, doi: 10.54543/fusion.v3i02.255.
P. Sukamto, Ispandi, A. S. Putra, N. Aisyah, and R. Toufiq, "Forensic Digital Analysis For CCTV Video Recording," International Journal Of Science, vol. 3, 2022.
S. Bahri and H. Kusindaryadi, "Design and Build Student Attendance Monitoring Using Facial Fingerprints Simultaneously Through Classroom CCTV," RESISTOR (Computer Electrical Power Telecommunication Control Electronics), vol. 3, no. 1, 2020.
W. Wang, X. Jiang, S. Wang, M. Wan, and T. Sun, "Identifying Video Forgery Process Using Optical Flow," In Lecture Notes in Computer Science (Including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 2014, pp. 244–257. doi: 10.1007/978-3-662-43886-2_18.
I. Riadi, A. Yudhana, and M. C. F. Putra, "Acquisition of Digital Evidence on Android-Based Instagram Messenger Using the National Institute of Justice (NIJ) Method," JUTISI (Journal of Informatics and Information Systems Engineering), vol. 4, 2018, Accessed: Sep. 24, 2024, doi: 10.28932/jutisi.v4i2.769.
Herman, A. Yudhana, and F. Anggraini, "Acquisition of Android-Based Tiktok Digital Evidence Using the National Institute of Justice Method," Journal of Information Technology and Computer Science (JTIIK), vol. 10, pp. 89–96, 2019, doi: 10.25126/jtiik.2023106416.
I. Riadi, R. Umar, and A. Firdonsyah, "Identification Of Digital Evidence On Android's Blackberry Messenger Using NIST Mobile Forensic Method," International Journal of Computer Science and Information Security (IJCSIS), vol. 15, 2017.
F. Albanna and I. Riadi, "Forensic Analysis of Frozen Hard Drive Using Static Forensics Method," International Journal of Computer Science and Information Security (IJCSIS), vol. 15, 2017.
G. H. A. Kusuma and I. N. Prawiranegara, "Digital Forensic Analysis of CCTV Video Footage Using Metadata and Hash," SISFOTEK (Information Systems and Technology), vol. 3, 2019.
D. Mualfah and R. A. Ramadhan, "Digital Forensic Analysis of CCTV Camera Footage Using the NIST (National Institute of Standards Technology) Method," IT Journal Research and Development, vol. 5, no. 2, pp. 171–182, Nov. 2020, doi: 10.25299/itjrd.2021.vol5(2).5731.
M. A. S. Nasr, M. F. AlRahmawy, and A. S. Tolba, "Multi-scale structural similarity index for motion detection," Journal of King Saud University - Computer and Information Sciences, vol. 29, no. 3, pp. 399–409, Jul. 2017, doi: 10.1016/j.jksuci.2016.02.004.
P. M. Kulkarni, B. Nautiyal, S. Kumar, R. Medidha, R. R. Savaliya, and M. Eknath, "IOT data Fusion framework for e-commerce," Measurement: Sensors, vol. 24, Dec. 2022, doi: 10.1016/j.measen.2022.100507.
D. N. Zhao, R. K. Wang, and Z. M. Lu, "Inter-frame passive-blind forgery detection for video shot based on similarity analysis," Multimed Tools Appl, vol. 77, no. 19, pp. 25389–25408, Oct. 2018, doi: 10.1007/s11042-018-5791-1.
T. Van Lanh, K. S. Chong, S. Emmanuel, and M. S. Kankanhalli, "A Survey On Digital Camera Image Forensic Methods," IEEE International Conference on Multimedia and Expo, pp. 16–19, 2007.
S. M. Pedapudi and N. Vadlamani, "Digital Forensics Approach For Handling Audio And Video Files," Measurement: Sensors, vol. 29, Oct. 2023, doi: 10.1016/j.measen.2023.100860.
V. Joshi and S. Jain, "Tampering Detection And Localization In Digital Video Using Temporal Difference Between Adjacent Frames Of Actual And Reconstructed Video Clip," International Journal of Information Technology (Singapore), vol. 12, no. 1, pp. 273–282, Mar. 2020, doi: 10.1007/s41870-018-0268-z.
P. Johnston, E. Elyan, and C. Jayne, "Video Tampering Localisation Using Features Learned From Authentic Content," Neural Comput Appl, vol. 32, no. 16, pp. 12243–12257, Aug. 2020, doi: 10.1007/s00521-019-04272-z.
M. C. Stamm, W. S. Lin, and K. J. R. Liu, "Temporal Forensics And Anti-Forensics For Motion Compensated Video," IEEE Transactions on Information Forensics and Security, vol. 7, no. 4, pp. 1315–1329, 2012, doi: 10.1109/TIFS.2012.2205568.
S. Jia, Z. Xu, H. Wang, C. Feng, and T. Wang, "Coarse-to-Fine Copy-Move Forgery Detection for Video Forensics," IEEE Access, vol. 6, pp. 25323–25335, Mar. 2018, doi: 10.1109/ACCESS.2018.2819624.
X. H. Nguyen, Y. Hu, M. A. Amin, K. G. Hayat, V. T. Le, and D. T. Truong, "Detecting Video Inter-Frame Forgeries Based on Convolutional Neural Network Model," International Journal of Image, Graphics and Signal Processing, vol. 12, no. 3, pp. 1–12, Jun. 2020, doi: 10.5815/ijigsp.2020.03.01.
R. C. Pandey, S. K. Singh, and K. K. Shukla, "Passive Copy- Move Forgery Detection in Videos," International Conference on Computer and Communication Technology (ICCCT, 2014.
N. Akhtar, M. Hussain, and Z. Habib, "DEEP-STA: Deep Learning-Based Detection and Localization of Various Types of Inter-Frame Video Tampering Using Spatiotemporal Analysis," Mathematics, vol. 12, no. 12, Jun. 2024, doi: 10.3390/math12121778.
L. Yu et al., "Exposing Frame Deletion By Detecting Abrupt Changes In Video Streams," Neurocomputing, vol. 205, pp. 84–91, Sep. 2016, doi: 10.1016/j.neucom.2016.03.051.
N. Akhtar, M. Saddique, K. Asghar, U. I. Bajwa, M. Hussain, and Z. Habib, "Digital Video Tampering Detection and Localization: Review, Representations, Challenges and Algorithm," Jan. 01, 2022, MDPI. doi: 10.3390/math10020168.
S. Kingra, N. Aggarwal, and R. D. Singh, "Inter-Frame Forgery Detection In H.264 Videos Using Motion And Brightness Gradients," Multimed Tools Appl, vol. 76, no. 24, pp. 25767–25786, Dec. 2017, doi: 10.1007/s11042-017-4762-2.
A. P. Justicia and I. Riadi, "Analysis of Forensic Video in Storage Data Using Tampering Method," International Journal of Cyber-Security and Digital Forensics, vol. 7, no. 3, pp. 328–335, 2018, doi: 10.17781/P002471.
M. Zampoglou et al., "Detecting Tampered Videos with Multimedia Forensics and Deep Learning," In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 2019, pp. 374–386. doi: 10.1007/978-3-030-05710-7_31.
P. Bestagini, S. Milani, M. Tagliasacchi, and S. Tubaro, "Local Tampering Detection In Video Sequences," International workshop on multimedia signal processing (MMSP), IEEE, 2013.
D. Yunita Sari, Y. Prayudi, and B. Sugiantoro, "Detection of Video Authenticity on Handycam Using the Localization Tampering Method," JOIN, vol. 2, no. 1, 2017.
I. Riadi, A. Yudhana, and R. V. A. Saputra, "Video Forensics on CCTV Using the Generic Computer Forensics Investigation Model (GCFIM) Framework," JURIKOM (Journal of Computer Research), vol. 10, no. 2, p. 540, Apr. 2023, doi: 10.30865/jurikom.v10i2.5888.
C. Long, A. Basharat, and A. Hoogs, "A Coarse-to-fine Deep Convolutional Neural Network Framework for Frame Duplication Detection and Localization in Forged Videos," CVPR Workshop, 2019.
J. Patel and R. Sheth, "An Optimized Convolution Neural Network Based Inter-Frame Forgery Detection Model-A Multi-Feature Extraction Framework", doi: 10.21917/ijivp.2021.0364.
Y. Liu and T. Huang, "Exposing Video Inter-Frame Forgery By Zernike Opponent Chromaticity Moments And Coarseness Analysis," Multimed Syst, vol. 23, no. 2, pp. 223–238, Mar. 2017, doi: 10.1007/s00530-015-0478-1.
R. Rigoni, P. G. Freitas, and M. C. Q. Farias, "Tampering Detection Of Audio-Visual Content Using Encrypted Watermarks," in Brazilian Symposium of Computer Graphic and Image Processing, IEEE Computer Society, Oct. 2014, pp. 196–203. doi: 10.1109/SIBGRAPI.2014.50.
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 ririn anggraini, Yudi Prayudi, Yudi Prayudi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.