Two-dimensional discrete fractional Fourier transform-based content removal algorithm

Abstract

This paper proposes a novel content removal technique for enhancing the camera identification performance. Here, very low bit rate videos with the overall noise patterns having time-varying statistics are considered. First, different two-dimensional discrete fractional Fourier transforms with different rotational angles are applied to the overall noise pattern of each frame of each video. Second, the modulus of each element of each transformed matrix is normalized to one if the rotational angles of the transforms are not equal to the integer multiples of $\pi$. Third, the corresponding two-dimensional inverse discrete fractional Fourier transform is applied to each normalized matrix, and the corresponding real part is taken out for the further processing. Fourth, the absolute values of the elements in each normalized real-valued matrix are bounded by certain threshold values. Here, different threshold values are employed for different rotational angles. Finally, the processed matrices are averaged over all the rotational angles and all the frames of the videos of the same camera. To evaluate the performance, the correlation function is employed. Extensive computer numerical simulations are preformed. The obtained results show that the proposed method outperforms existing methods (Kang et al. in IEEE Trans Inf Forensics Secur 7(2):393–402, 2012; Li in IEEE Trans Inf Forensics Secur 5(2):280–287, 2010).