A Web-based Identification Method for Illegal Streaming Videos Using Low-frequency Components of the Fast Fourier Transform
DOI:
https://doi.org/10.13052/jwe1540-9589.2461Keywords:
Illegal streaming video, low-frequency component, fast Fourier transform, real-time video identification, copyright protectionAbstract
With the proliferation of web-based content platforms, the distribution of illegally streamed videos poses a serious threat to the reliability of web applications and the integrity of content copyright protection systems. Traditional video identification methods typically require the processing of large-scale feature data, which hinders the real-time performance, lightweight nature, and scalability demanded by web environments. In this paper, we propose a method for identifying illegally streamed videos that is optimized for efficient operation within web systems. The proposed approach utilizes only the low-frequency components of the fast Fourier transform (FFT). By transforming video frames into the frequency domain and extracting the structurally significant low-frequency components, the method replaces high-dimensional feature data with more compact representations. This allows the system to maintain low computational complexity and fast response times, even in web application environments. Experimental results demonstrate that, compared to existing methods, the proposed technique achieves up to 93 times reduction in feature data size, a recognition rate of 98%, and an average response time of 1745 ms. From the perspective of web engineering, the proposed method holds strong potential as a real-time identification module in web-based copyright protection systems. It offers a balanced approach that satisfies both lightweight processing requirements and high accuracy.
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References
I. Yoo, J. Lee, S. Jang, B. Park, S. Kim, and Y. Kim, “An illegal distribution platform tracking method using IP port scanning,” Journal of Software Assessment and Valuation, vol. 19, no. 4, pp. 115–122, 2023. DOI: 10.29056/jsav.2023.12.12.
D. J. Spajic, “Piracy is back: Piracy statistics for 2024,” DataProt, Feb. 2024. [Online]. Available: https://dataprot.net/statistics/piracy-statistics/.
C. H. Kim, H. J. Yu, S. Y. Kim, and S. H. Oh, “Efficient techniques to block copyright infringement illegal streaming sites,” Journal of KIISC, vol. 32, no. 5, pp. 837–844, 2022. DOI: 10.13089/JKIISC.2022.32.5.837.
G. Yoon, Y. Lee, and S. Choi, A Study on the Improvement of the Prevention System of Illegal Harmful Information Distribution, Korea Communications Commission, Rep. KCC-2023-33, 2023.
M. Nickel, L. Kalms, T. Haring, and D. Göhringer, “High-performance AKAZE implementation including parametrizable and generic HLS modules,” in Proc. IEEE ASAP, 2022, pp. 139–147. DOI: 10.1109/ASAP54787.2022.00031.
D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis., vol. 60, no. 2, pp. 91–110, 2004. DOI: 10.1023/B:VISI.0000029664.99615.94.
H. Bay, T. Tuytelaars, and L. Van Gool, “SURF: Speeded Up Robust Features,” in ECCV, Lecture Notes in Computer Science, vol. 3951, Springer, Berlin, Heidelberg, 2006, pp. 404–417.
E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, “ORB: An efficient alternative to SIFT or SURF,” in Proc. IEEE Int. Conf. Computer Vision (ICCV), 2011, pp. 2564–2571. DOI: 10.1109/ICCV.2011.6126544.
S. Paul, S. Kumawat, A. Gupta, and D. Mishra, “F2former: When fractional Fourier meets deep Wiener deconvolution and selective frequency transformer for image deblurring,” arXiv preprint, arXiv:2409.02056, 2024. DOI: 10.48550/arXiv.2409.02056.
R. C. Gonzalez and R. E. Woods, Digital Image Processing, 4th ed., Pearson, 2018.
A. K. Jain, Fundamentals of Digital Image Processing, Prentice-Hall, 1989.
J. Hu, F. Jia, and W. Liu, “Application of Fast Fourier Transform,” Highlights in Science, Engineering and Technology, vol. 38, pp. 590–595, 2023.
D. Acharya, A. Billimoria, N. Srivastava, and A. Bhardwaj, “Emotion recognition using Fourier transform and genetic programming,” Applied Acoustics, vol. 165, 107328, 2020. DOI: 10.1016/j.apacoust.2020.107328.
S. Kiruthika and V. Masilamani, “Image quality assessment based fake face detection,” Multimedia Tools and Applications, vol. 81, pp. 12345–12367, 2022. DOI: 10.1007/s11042-021-11000-z.
B. Nassi, R. Ben-Netanel, A. Shamir, and Y. Elovici, “Game of Drones: Detecting streamed POI from encrypted FPV channel,” in Proc. ACM SIGSAC Conf. Computer and Communications Security, 2018, pp. 123–137. DOI: 10.1145/3243734.3243783.
Y. Zhao and P. Krähenbühl, “Real-time online video detection with temporal smoothing transformers,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), 2022, pp. 1234–1243. DOI: 10.1109/CVPR52688.2022.00131.
