#Newsbin disable duplicate detector series#

This chapter proposes an automated electric network frequency based audio forensic scheme that monitors abrupt mutations of tampered frames and discontinuities in the variations of electric network frequency features. In addition, most forensic algorithms do not provide high detection precision in adverse signal-to-noise conditions. However, current electric network based audio forensic schemes are limited by their inability to obtain concurrent electric network frequency reference datasets from power grids. The proposed method is also robust against common post-processing operations such as noise addition, filtering operation, and especially compression operation.Дigital media forensics can exploit the electric network frequency of audio signals to detect tampering. 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. 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. The voiced parts, which give minimum ED are determined as copy-move audio parts. Euclidean distance (ED) is applied to compute the similarities between the features. These MDCT features are very robust against commonly used post-processing operations (especially audio compressing). 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. For this purpose, we first divide the speech recording into voiced and unvoiced parts by extracting a pitch sequence from the speech recording. In this study, we propose a robust method for detection and localization of the audio copy-move forgery using modified discrete cosine transform (MDCT). However, post-processing operations applied to forged speech to hide traces of forgery make detection of forensic more difficult. Extensive experiments show that the best detection accuracy of ASLNet for the intradatabase and cross-database evaluation can achieve 0.9965 and 0.9740 receptively, which outperforms the state-of-the-art method.Д¬opy-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. We evaluate the effectiveness of the proposed ASLNet on four datasets produced from publicly available speech corpus. Finally, we compare ПЃ with the predetermined threshold T to determine whether the corresponding audio segment is spliced.

Next, the binary output mask is divided into small segments according to the L slr, and the ratio ПЃ of the number of elements equal to one to the number of all elements in a small segment is calculated.

#Newsbin disable duplicate detector series#

Then, we concatenate acoustic feature matrices from all segments of an audio clip into an acoustic feature matrix and send it to a fully convolutional network (FCN) based encoder-decoder architecture which consists of a series of convolutional, pooling and transposed convolutional layers to get a binary output mask. Firstly, an audio clip is divided into several small audio segments according to the size of the smallest localization region L slr, and the acoustic feature matrix and corresponding binary ground truth mask are created from each audio segment. In this paper, a novel audio splicing detection and localization method based on an encoder-decoder architecture (ASLNet) is proposed. Audio splicing means inserting an audio segment into another audio, which presents a great challenge to audio forensics.