Rangding Wang

Multiple audio watermarks based on lifting wavelet transform

An audio watermarking scheme, which embedded robust and fragile watermark at the same time in lifting wavelet domain is presented. Robust watermark is embedded in the low frequency range using mean quantization. It has great robustness and imperceptibility. Fragile watermark is embedded in the high frequency range by quantizing single coefficient. When the audio signal is tampered, the watermark information will change synchronously. So it can be used for audio content integrity verification. The watermark can be extracted without the original digital audio signal. Experimental results show that robust watermark is robust to many attacks, such as mp3 compression, low pass filtering, noise addition, requantization, resampling and so on. Fragile watermark is very sensitive to these attacks.

Steganalysis of MP3Stego with low embedding-rate using Markov feature

MP3Stego is a typical steganographic tool for MP3 audios, whose embedding behavior disturbs the intrinsic correlation of the quantized MDCT coefficients (refer to as QMDCTs). In this paper, the Markov feature captured this correlation were designed based on the QMDCTs. The feature is sensitive to the subtle alteration caused by MP3stego embedding even at a low embedding-rate. In addition, some work on QMDCT pre-processing, threshold selection and feature optimization were applied to feature construction, which contribute to improving the detection accuracy and reducing the computational complexity of the proposed scheme. Experimental results show that our approach can effectively detect MP3Stego of low embedding-rate and outperforms the prior arts.

An efficient algorithm for double compressed AAC audio detection

As a new generation of compression encoding standard, MPEG-2/4 Advanced Audio Coding (AAC) would be a widely-used audio format in the near future. However, these AAC audios often be forged by audio forgers for their own benefits in some significant events, which will cause double AAC compression. In this paper, the probability values and Markov features based on the Huffman codebook indexes of AAC audio were constructed and an efficient algorithm is proposed to detect double compression. Experimental results demonstrate that our method has high detection accuracy and low computational complexity. To the best of our knowledge, it is the first time to apply double compression detection to AAC audio forensics.

Tunable data hiding in partially encrypted H.264/AVC videos

The encryption of Luma IPMs is designed in addition to residual and motion vector.Data embedding is done on encrypted videos, which preserves the confidentiality.The bin-strings of abs_level and abs_MVD are exploited for data hiding flexibly.The hidden data can be extracted either in encrypted domain or decrypted domain. An improved scheme of data hiding directly in partially encrypted H.264/AVC videos using CABAC bin-string substitution is proposed. The encryption of Luma prediction modes is designed in addition to residual encryption and motion vector encryption in order to significantly improve the structural deterioration. Both the bin-strings of abs_level and the bin-strings of abs_MVD are exploited for data hiding to provide a higher flexibility for users to select the tradeoff between hiding capacity and video quality. Since the data embedding is done in the encrypted domain, the proposed scheme preserves the confidentiality of video content. With an encrypted video containing the hidden data, the receiver can accomplish data extraction directly in encrypted domain using only the data-hiding key, or obtain a decrypted video similar to the original version using only the cryptographic key. Experimental results have demonstrated that the proposed scheme can achieve better scrambling performance and higher embedding capacity.

Detection of Double Compression for HEVC Videos Based on the Co-occurrence Matrix of DCT Coefficients

Detection of double video compression can reveal partly the video processing history, which is considered to be an important auxiliary means used in video forensics. In this paper, by analyzing the distribution of quantized discrete cosine transform (DCT) coefficients, we propose a scheme to detect double HEVC videos compression under different quantization parameter (QP) values. Firstly, four 5 × 5 co-occurrence matrixes were derived from DCT coefficients along four directions respectively, i.e., horizontal, vertical, main diagonal and minor diagonal. Then other 9 high order statistics were constructed by each co-occurrence matrix. Finally, a 136 dimensions feature set constitutes all elements of 5 × 5 co-occurrence matrix and other 9 higher order statistics which was sent to support vector machine (SVM). It is used to identify whether video has been gone through double compression. Experiment results show the effectiveness of the algorithm.

Separable Reversible Data Hiding in Encrypted Images Based on Two-Dimensional Histogram Modification

An efficient method of completely separable reversible data hiding in encrypted images is proposed. The cover image is first partitioned into nonoverlapping blocks and specific encryption is applied to obtain the encrypted image. Then, image difference in the encrypted domain can be calculated based on the homomorphic property of the cryptosystem. The data hider, who does not know the original image content, may reversibly embed secret data into image difference based on two-dimensional difference histogram modification. Data extraction is completely separable from image decryption; that is, data extraction can be done either in the encrypted domain or in the decrypted domain, so that it can be applied to different application scenarios. In addition, data extraction and image recovery are free of any error. Experimental results demonstrate the feasibility and efficiency of the proposed scheme.

Source Cell-Phone Identification Using Spectral Features of Device Self-noise

Source cell-phone identification has become a hot topic in multimedia forensics recently. In this paper, we propose a novel cell-phone identification method based on the recorded speech files. Device self-noise is considered as the fingerprint of the cell-phone, and the self-noise is estimated from the near-silent segments of recording. Moreover, two categories of spectral features of self-noise, i.e., spectral shape features (SN-SSF) and spectral distribution features (SN-SDF), are extracted for closed-set classification using SVM classifier. Experimental results show that the self-noise has the ability to identify the cell-phones of 24 different models, and identification accuracies of 89.23% and 94.53% have been obtained for SN-SSF and SN-SDF, respectively. To the best of our knowledge, it is the first attempt to comprehensively define the self-noise of cell-phone and furthermore apply it to source identification issue of audio forensics.

Two-Dimensional Histogram Modification for Reversible Data Hiding in Partially Encrypted H.264/AVC Videos

Due to the security and privacy-preserving requirements from cloud computing platforms, it is sometimes desired that the video content is accessible in encrypted form. Reversible data hiding in the encrypted domain has gained increasing attention, as it can perform data hiding in encrypted medias without decryption which preserves the confidentiality of the content. In this paper, an efficient reversible data hiding scheme for encrypted H.264/AVC videos is proposed. During H.264/AVC encoding, the intra-prediction mode (IPM), motion vector difference (MVD), and the sign bits of residue coefficients are encrypted using a standard stream cipher. Then, every two adjacent coefficients are grouped into coefficient pairs. Based on the resulting coefficient pairs, the data-hider may reversibly embed secret data into the encrypted H.264/AVC video by using two-dimensional histogram modification. In addition, dynamic threshold is utilized to determine the embedding region. With an encrypted video containing hidden data, data extraction can be carried out either in encrypted or decrypted domain. Experimental results demonstrate the superiority of the proposed scheme.

Audio Tampering Detection Based on Quantization Artifacts

MP3 is one of the common formats in the recording equipments. The authenticity and integrity of MP3 audio is widely concerned. We analyzed quantization characteristic of MP3 encoding, and studied the effect of quantified in frame offset and non offset frame. Then we combined statistical characteristics of zero spectral coefficients before and after quantization. Finally, a tampering detection method based on quantitative characteristics is proposed. According to the disadvantage of existing frame offset method that cannot detect high rate compression, this paper in the quantization artifacts description according to the characteristics of the line frequency distribution used in front of the 16 band. We can further effectively solve tamper detection problem of high bit rate compression by studying 16 band quantization. The experimental results show that the accuracy rate of proposed detection method can reach up to 99 %, the compression rate of detection can reach 256 Kbps, and the complexity of compared with the existing methods is significantly reduced.

Multiple MP3 Compression Detection Based on the Statistical Properties of Scale Factors

MP3 audio is one of the most popular audio formats, and it is easy to be tampered. By revealing the historical traces of the audio compression, it could be speculated effectively whether the suspicious MP3 audio has been tampered or not. Scale factor is an important parameter in MP3 encoding. In this paper, the variation law of the scale factor with various times of the compression is discovered by analyzing its statistical properties. The proposed method can be applied to detect multiple compression operations when the bit rates of the second and third compression are the same. The experimental results show that the proposed approach has good performance and higher accuracy with respect to the state-of-art.