Call for papers for Special Issue of ACM Transactions on Multimedia Computing, Communications and Applications on Interactive Digital Television

ACM Transactions on Multimedia Computing, Communications and Applications (ACM TOMCCAP)

Detecting the camera model used to shoot a picture enables to solve a wide series of forensic problems, from copyright infringement to ownership attribution. For this reason, the forensic community has developed a set of camera model identification algorithms that exploit characteristic traces left on acquired images by the processing pipelines specific of each camera model. In this letter, we investigate a novel approach to solve camera model identification problem. Specifically, we propose a data-driven algorithm based on convolutional neural networks, which learns features characterizing each camera model directly from the acquired pictures. Results on a well-known dataset of 18 camera models show that: 1) the proposed method outperforms up-to-date state-of-the-art algorithms on classification of 64 × 64 color image patches; 2) features learned by the proposed network generalize to camera models never used for training.

First Steps Toward Camera Model Identification with Convolutional Neural Networks

Detection of double joint photographic experts group (JPEG) compression is of great significance in the field of digital forensics. Some successful approaches have been presented for detecting double JPEG compression when the primary compression and the secondary compression have different quantization matrixes. However, when the primary compression and the secondary compression have the same quantization matrix, no detection method has been reported yet. In this paper, we present a method which can detect double JPEG compression with the same quantization matrix. Our algorithm is based on the observation that in the process of recompressing a JPEG image with the same quantization matrix over and over again, the number of different JPEG coefficients, i.e., the quantized discrete cosine transform coefficients between the sequential two versions will monotonically decrease in general. For example, the number of different JPEG coefficients between the singly and doubly compressed images is generally larger than the number of different JPEG coefficients between the corresponding doubly and triply compressed images. Via a novel random perturbation strategy implemented on the JPEG coefficients of the recompressed test image, we can find a “proper” randomly perturbed ratio. For different images, this universal “proper” ratio will generate a dynamically changed threshold, which can be utilized to discriminate the singly compressed image and doubly compressed image. Furthermore, our method has the potential to detect triple JPEG compression, four times JPEG compression, etc.

Detecting Double JPEG Compression With the Same Quantization Matrix

https://dr.ntu.edu.sg/bitstream/10220/40556/1/Twenty%20years%20of%20digital%20audio%20watermarking%20-%20A%20comprehensive%20review.pdf

Twenty years of digital audio watermarking-a comprehensive review

A sparse stacked denoising autoencoder with optimized transfer learning applied to the fault diagnosis of rolling bearings

The development of a geometric invariant audio watermarking scheme without degrading acoustical quality is challenging work. This paper proposes a multi-bit spread-spectrum audio watermarking scheme based on a geometric invariant log coordinate mapping (LCM) feature. The LCM feature is very robust to audio geometric distortions. The watermark is embedded in the LCM feature, but it is actually embedded in the Fourier coefficients which are mapped to the feature via LCM, so the embedding is actually performed in the DFT domain without interpolation, thus eliminating completely the severe distortion resulted from the non-uniform interpolation mapping. The watermarked audio achieves high auditory quality in both objective and subjective quality assessments. A mixed correlation between the LCM feature and a key-generated PN tracking sequence is proposed to align the log-coordinate mapping, thus synchronizing the watermark efficiently with only one FFT and one IFFT. Both the theoretical analysis and experimental results show that the proposed audio watermarking scheme is not only resilient against common signal processing operations, including low-pass filtering, MP3 recompression, echo addition, volume change, normalization, test functions in the Stirmark benchmark, and DA/AD conversion, but also has conquered the challenging audio geometric distortion and achieves the best robustness against simultaneous geometric distortions, such as pitch invariant time-scale modification (TSM) by ±20%, tempo invariant pitch shifting by 20%, resample TSM with scaling factors between 75% and 140%, and random cropping by 95%. This is mainly contributed by the proposed geometric invariant LCM feature. To our best knowledge, audio watermarking based on LCM has not been reported before.

Geometric Invariant Audio Watermarking Based on an LCM Feature

MP3 is the most popular compressed audio format in our daily life but it can be doctored very easily by pervasive audio editing software. Thus it is necessary to develop authentication methods for MP3. Different from JPEG compression for image, MP3 compression has its own characteristics. Thus existing forensics methods for JPEG compression is unable to be applied to MP3 compression directly. In this paper, we propose a passive approach to detect doctored MP3 audio by checking frame offsets. As the audio samples are divided into frames to encode, each frame has its own frame offset after encoding. Forgeries lead to the broken of frame grids. So the frame offsets are good indication for locating forgeries, and the frame offsets can be detected by the identification of quantization characteristic. In this way, the doctored positions can be automatically located. Experimental results demonstrate the validity of the proposed approach on detecting some common forgeries, such as deletion, insertion, substitution and splicing. Under different bitrates, the detection ratios are above 94%. To the best of our knowledge, this piece of work is the first one to investigate digital forensics on MP3 format.

Detecting digital audio forgeries by checking frame offsets

MP3 is the most popular audio format nowadays in our daily life,e.g., music on the Internet are often of this format. However, low bit rate MP3s are sometimes transcoded to high bit rate, resulting in fake-quality MP3, to seek commercial benefit. This paper presents a method to defeat this kind of fake-quality MP3. Our experimental study and theoretical analysis have demonstrated that there are fewer MDCT coefficients of small values in fake-quality MP3 than in normal MP3. We use the numbers of small-value MDCT coefficients as features to discriminate fake-quality MP3 from normal MP3. Experimental results show the effectiveness of the proposed method. To our best knowledge, this work is the first one to find out fake-quality MP3.

Defeating fake-quality MP3

MP3 is the most popular audio format nowadays in our daily life, for example music downloaded from the Internet and file saved in the digital recorder are often in MP3 format. However, low bitrate MP3s are often transcoded to high bitrate since high bitrate ones are of high commercial value. Also audio recording in digital recorder can be doctored easily by pervasive audio editing software. This paper presents two methods for the detection of double MP3 compression. The methods are essential for finding out fake-quality MP3 and audio forensics. The proposed methods use support vector machine classifiers with feature vectors formed by the distributions of the first digits of the quantized MDCT (modified discrete cosine transform) coefficients. Extensive experiments demonstrate the effectiveness of the proposed methods. To the best of our knowledge, this piece of work is the first one to detect double compression of audio signal.

Detecting double compression of audio signal

The adaptive multi-rate (AMR) audio codec adopted by many portable recording devices is widely used in speech compression The use of AMR speech recordings as evidence in court is growing Nowadays, it is easy to tamper with digital speech recordings, which makes audio forensics increasingly important The detection of double compressed audio is one of the key issues in audio forensics In this paper, we propose a framework for detecting double compressed AMR audio based on the stacked autoencoder (SAE) network and the universal background model—Gaussian mixture model (UBM-GMM) Instead of hand-crafted features, we used the SAE to learn the optimal features automatically from the audio waveforms Audio frames are used as network input and the last hidden layer’s output constitutes the features of a single frame For an audio clip with many frames, the features of all the frames are aggregated and classified by UBM-GMM Experimental results show that our method is effective in distinguishing single/double compressed AMR audio and outperforms the existing methods by achieving a detection accuracy of 98% on the TIMIT database Exhaustive experiments demonstrate the effectiveness and robustness of the proposed method

Rui Yang

Papers

Geometric Invariant Audio Watermarking Based on an LCM Feature

Detecting digital audio forgeries by checking frame offsets

Defeating fake-quality MP3

Detecting double compression of audio signal

Detection of Double Compressed AMR Audio Using Stacked Autoencoder