1. SFT
  2. Archiwum
  3. Vol. 63
  4. An image Analysis Algorithm for Detec...

An image Analysis Algorithm for Detecting Modified Content on the Internet Against Cybercrime. A Technique for Estimating the Probability of Modification

Karol, Jędrasiak, Ph.D. Eng. , Robert, Wolanski, Ph.D. Eng.

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Open Access
DOI: 10.12845/sft.63.1.2024.7, pages: 88 – 94

Abstract

Aim: The aim of this study is to present a cutting-edge image analysis algorithm designed to estimate the probability of modifications in digital images, a critical component in cybersecurity for detecting altered content on the Internet. This technique enhances the accuracy of change detection by distinguishing real inconsistencies from accidental changes. As the problem of sophisticated image processing software and fake images continues to rise on the Internet, the issue of image authenticity becomes increasingly crucial in many areas of science and society. Traditional visual inspection methods are inadequate, as research shows human perception is limited in recognizing the authenticity of images. This study aims to improve the accuracy of detecting modifications by distinguishing authentic inconsistencies from random anomalies

Project and methods: This project involves developing an algorithm that leverages noise analysis and a statistical validation step to detect image modifications. The algorithm employs the False Positive Rate Index (FPRI) to manage false positives, providing a reliable confidence level for each detection. The method includes visual exploration to interpret detections and compares the algorithm’s performance with other state-of-the-art techniques in scenarios such as retouching, coloring, and merging. The algorithm analyzes noise in images, considering changes in its statistical properties due to image processing like noise reduction, demosaicing, chromatic aberration correction, color matching, gamma correction, and image compression. It compares global noise curves with local ones to identify potential changes, with results visualized using heat maps.

Results: The algorithm demonstrates promising performance across various test scenarios, successfully identifying true modifications among random events. However, it shows limitations in detecting certain types of forgeries, such as internal copy transfer and merging in high-noise areas. Tests conducted on various data sets, including scenarios of retouching, coloring, and merging, confirm the algorithm’s effectiveness in detecting modifications. The results are compared with other noise analysis methods such as Splicebuster, Noiseprint, and Mahdian, showing superior performance in most cases.

Conclusions: The proposed method represents a significant advance in image forgery detection within the field of cybersecurity, offering rigorous statistical validation and a measurable level of detection confidence. Despite some challenges in detecting specific types of manipulation, the algorithm is a valuable tool in digital image analysis and forensic research, enhancing the reliability of image alteration detection. As the use of digital technologies increases, so does the importance of image interpretation in areas like fire investigations and other incidents involving records from both classical and thermographic imaging systems. This method is particularly valuable in forensic investigations, where objective evidence of image authenticity is crucial. Future challenges include the use of neural networks to replicate the characteristic noise of original images to hide alterations, underscoring the need for continued development of advanced forgery detection techniques.

Keywords: wykrywanie modyfikacji obrazu, cyberbezpieczeństwo, analiza obrazu, wykrywanie fałszywych obrazów

Type of article: original scientific article

Bibliography:

  1. Farid H., Image forgery detection, “IEEE Signal processing magazine” 2009 26(2), 16–25, https://doi.org/10.1109/MSP.2008.931079.
  2. Saber A.H., Khan M.A., Mejbel B.G., A survey on image forgery detection using different forensic approaches, “Advances in Science, Technology and Engineering Systems Journal” 2020, 5(3), 361–370, https://doi.org/10.25046/aj050347.
  3. Chen Y., Thing V.L., A study on the photo response non-uniformity noise pattern based image forensics in real-world applications, in Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition, The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), 2012.
  4. Li Y., Li Z., Wei K., Xiong W., Yu J., Qi B., Noise estimation for image sensor based on local entropy and median absolute deviation, “Sensors" 2019, 19 (2), 339, https://doi.org/10.3390/s19020339.
  5. Pun C.M., Liu B., Yuan X.C., Multi-scale noise estimation for image splicing forgery detection, “Journal of visual communication and image representation” 2016, 38, 195–206, https://doi.org/10.1016/j.jvcir.2016.03.005.
  6. Muzaffer G., Ulutas G., A new deep learning-based method to detection of copy-move forgery in digital images, in: Scientific meeting on Electrical-Electronics & Biomedical Engineering and computer science, IEEE, 1–4, https://doi.org/10.1109/EBBT.2019.8741657.
  7. Cozzolino D., Poggi G., Verdoliva L,. Splicebuster: A new blind image splicing detector, in: IEEE International Workshop on Information Forensics and Security (WIFS), IEEE, Rome 2014, 1–6, https://doi.org/10.1109/WIFS.2015.7368565.
  8. Cozzolino D., Verdoliva, L., Noiseprint: A CNN-based camera model fingerprint, IEEE Transactions on Information Forensics and Security, 2019, 1–1, https://doi.org/10.1109/ TIFS.2019.2916364 . [9] Mahdian B., Saic S., A bibliography on blind methods for identifying image forgery, “Signal Processing: Image Communication” 2020, 25(6), 389–399, https://doi.org/10.1016/j.image.2010.05.003.
  9. Rezatofighi H., Tsoi N., Gwak J., Sadeghian A., Reid I., Savarese S., Generalized intersection over union: A metric and a loss for bounding box regression, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, 658–666.
  10. Sasaki Y, The truth of the F-measure, “Teach tutor mater” 2007, 1(5), 1–5.
  11. Jędrasiak K., Analiza zagrożeń bezpieczeństwa współczesnych kanałów komunikacyjnych i ich wpływu na bezpieczeństwo, II Ogólnopolska Konferencja Naukowa pt. Nauki społeczne wobec współczesnych wyzwań i kryzysów Aspekt prawny, socjologiczny i aksjologiczny Bezpieczeństwo w ujęciu holistycznym, 14.12.2023 r., Ełk.
  12. Jędrasiak K., Investigating the risk factors of unethical use of content modification and falsification methods, Ogólnopolska Konferencja Naukowa pt. „Bezpieczeństwo RP w wymiarze narodowym i międzynarodowym – wnioski z ostatniej dekady”, 5–6.12.2023, Rynia.
  13. Dixit P., Silakari S., Deep learning algorithms for cybersecurity applications: A technological and status review, “Computer Science Review” 2020, 39 (4), 100317, https://doi.org/10.1016/j.cosrev.2020.100317.
  14. Tyagi S., Yadav D., A detailed analysis of image and video forgery detection techniques, „The Visual Computer” 2022, 39 (3), 813–833, https://doi.org/10.1007/s00371-021-02347-4.
  15. Pour M.S., Nader C., Friday, K., Bou-Harb E., A comprehensive survey of recent internet measurement techniques for cyber security, „Computers & Security” 2023, 128, 103123, https://doi.org/10.1016/j.cose.2023.103123.
  16. Stamm M.C., Liu K.R., Forensic estimation and reconstruction of a contrast enhancement mapping, in: IEEE International Conference on Acoustics, Speech and Signal Processing, 2010, 1698–1701, https://doi.org/10.1109/ ICASSP.2010.5495488.
  17. Kondepudy R., Healey G., Use of invariants for recognition of three-dimensional color textures, „JOSA A” 1994, 11(11), 3037–3049.
  18. Bayar B., Stamm, M.C., 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, https://doi.org/10.1145/2909827.2930786.