Ustubioglu, Beste; Küçükuğurlu, Büşranur; Ulutas, Guzin

Copy-move forgery is one of the most widely used methods in the field of audio forensic because it is difficult to detect and convenient to apply. However, post-processing operations applied to forged speech to hide traces of forgery make detection of forensic more difficult. In this study, we propose a robust method for detection and localization of the audio copy-move forgery using modified discrete cosine transform (MDCT). For this purpose, we first divide the speech recording into voiced and unvoiced parts by extracting a pitch sequence from the speech recording. After the determination of voiced parts, we extract MDCT coefficients from them and take the mean of the transpose of the coefficient matrix as the feature. These MDCT features are very robust against commonly used post-processing operations (especially audio compressing). Euclidean distance (ED) is applied to compute the similarities between the features. The voiced parts, which give minimum ED are determined as copy-move audio parts. Also, two separate databases (Pitch-based and Voice Activity Detection (VAD-based) are created during the work to evaluate the performance of the proposed method because there is no common database in the field of audio forensic in the literature. Experiment results show that the proposed method gives better results for detection and localization of audio copy-move forgery on different databases compared to other studies in the literature. The proposed method is also robust against common post-processing operations such as noise addition, filtering operation, and especially compression operation.

Küçükuğurlu, Büşranur; Gedikli, Eyüp

Thresholding is a frequently used method in image processing because of its consistency and low computational cost. Otsu's and Kapur's methods are two important techniques that were proved to be best thresholding methods. However, they have high computational complexity when extended to multilevel thresholding because of their exhaustively search. Recently, meta-heuristic algorithms have been successfully applied for thresholding problems. In this study, six different meta-heuristic algorithms based on Otsu's and Kapur's functions; Particle Swarm Optimization (PSO), Firefly Algorithm (FA), Symbiotic Organisms Search (SOS), Artifical Bee Colony (ABC), Genetic Algorithm (GA) and grey Wolf Optimizer (GWO) were used for multilevel thresholding problem and compared. Experimental results suggest that SOS, PSO and FA algorithms often have higher fitness values than other algorithms. Especially when more than two threshold values are determined, SOS algorithm mostly gives higher fitness values. PSNR and SSIM results of the algorithms are similar. In terms of computational complexity, the GWO algorithm has the fastest convergence. For standard deviations of objective functions; more stable results were obtained with SOS based on Kapur's function, SOS and PSO based on Otsu's function. Also, SOS based on Kapur's function was found to be the most successful algorithm in the Friedman test. As a result, although the GWO approached faster, the SOS algorithm produced more consistent results for both objective functions.