Image splicing is one of the most common image tampering operations, where the content of the tampered image usually significantly differs from that of the original one. As a consequence, forensic methods aiming to locate the spliced areas are of great realistic significance. Among these methods, the noise based ones, which utilize the fact that images from different sources tend to have various noise levels, have drawn much attention due to their convenience to implement and the relaxation of some operation specific assumptions. However, the performances of the existing noise based image splicing localization methods are unsatisfactory when the noise difference between the original and spliced regions is relatively small. In this paper, through incorporation of a recent developed noise level estimation algorithm, we propose an effective image splicing localization method. The proposed method performs blockwise noise level estimation of a test image with principal component analysis (PCA)-based algorithm, and segments the tampered region from the original region by kmeans clustering. The experimental results demonstrate the superiority of the proposed method over several state-of-the-art methods, especially for practical image splicing, where the noise difference between the original and spliced regions is typically small.

No abstract

No abstract

No abstract

This article deals with the problem of Electric Network Frequency (ENF) estimation where Signal to Noise Ratio (SNR) is an essential challenge. By exploiting the low-rank structure of the ENF signal from the audio spectrogram, we propose an approach based on robust principle component analysis to get rid of the interference from speech contents and some of the background noise, which in our case can be regarded as sparse in nature. Weighted linear prediction is enforced on the low-rank signal subspace to gain accurate ENF estimation. The performance of the proposed scheme is analyzed and evaluated as a function of SNR, and the Cramér-Rao Lower Bound (CRLB) is approached at an SNR level above -10 dB. Experiments on real datasets have demonstrated the advantages of the proposed method over state-of-the-art work in terms of estimation accuracy. Specifically, the proposed scheme can effectively capture the ENF fluctuations along the time axis using small numbers of signal observations while preserving sufficient frequency precision.

No abstract

No abstract

In this work, we investigate how features can be effectively learned by deep neural networks for audio forensic problems. By providing a preliminary feature preprocessing based on Electric Network Frequency (ENF) analysis, we propose a convolutional neural network (CNN) for training and classification of genuine and recaptured audio recordings. Hierarchical representations which contain levels of details of the ENF components are learned from the deep neural networks and can be used for further classification. The proposed method works for small audio clips of 2 seconds' duration, whereas the state of the art may fail with such small audio clips. Experimental results demonstrate that the proposed network yields high detection accuracy with each ENF harmonic component represented as a single-channel input. The performance can be further improved by a combined input representation which incorporates both the fundamental ENF and its harmonics. Convergence property of the network and the effect of using analysis window with various sizes are also studied. Performance comparison against the support tensor machine demonstrates the advantage of using CNN for the task of audio recapture detection. Moreover, visualization of the intermediate feature maps provides some insight into what the deep neural networks actually learn and how they make decisions.Index Terms-Electric network frequency, convolutional neural network, audio recapture detection.