The forensic investigation of JPEG compression generally relies on the analysis of first-order statistics based on image histogram. The JPEG compression detection methods based on such methodology can be effortlessly circumvented by adopting some anti-forensic attacks. This paper presents a counter JPEG anti-forensic method by considering the second-order statistical analysis based on the co-occurrence matrices (CMs). The proposed framework comprises three stages: selection of the target difference image, evaluation of CMs, and generation of second-order statistical feature based on CMs. In the first stage, we explore the effects of dithering operation of JPEG anti-forensics by analyzing the variance inconsistencies along the diagonals. Afterward, CMs are evaluated in the second stage to highlight the effects of grainy noise introduced during the dithering operation. The third stage is devoted to generate an optimal second-order statistical feature which is fed to the SVM classifier. The experimental results based on the uncompressed color image database and BOSSBase dataset images demonstrated that the proposed forensic detector based on CM is very efficient even in the presence of anti-forensic attacks. Moreover, the experimental results also confirm the competency of the proposed method in counter median filtering and contrast enhancement anti-forensics. The proposed scheme also provides satisfactory results in detecting other image processing operations such as mean filtering, Gaussian filtering, Weiner filtering, scaling, and rotation, thereby revealing its multi-purpose nature.

In this paper, data hiding algorithm by using lifting scheme and genetic algorithm (GA) has been proposed. Arnold transform has been used to scramble the secret image to secure the extraction of secret image. Lifting scheme is applied on the cover image to get the wavelet subbands. In this algorithm, scrambled secret image is embedded into significant wavelet coefficients of subbands of cover image. Scaling factor (SF) parameter is used in embedding and extracting process of the proposed algorithm and GA is used to optimise this parameter. This optimisation is used to maximise the value of peak signal to noise ratio (PSNR) of composite image and similarity index modulation (SIM) of extracted secret image. Experimental results reveal that proposed algorithm provides high embedding capacity and better quality of composite images than the existing data hiding techniques. To show the effectiveness of the proposed algorithm, statistical tests have been performed to show that the imperceptibility is maintained.

As a generalisation of fractional Fourier transform, Fresnel transform and Fourier transform, the linear canonical transform (LCT) is a four parameter class of integral transform and has been used in many fields of optics and signal processing. In this study, the authors present a model of multiplicative filtering for the band‐limited signals in the LCT domain by using the convolution theorem given in the literature. Finally, practical applications of filtering in LCT domain are discussed based on the presented model of multiplicative filtering and results are compared with that of frequency domain filtering and fractional domain filtering. It is found from the simulation results that mean square error is minimum for different values of signal‐to‐noise ratio in case of LCT domain filtering.