Double Compressed Research Articles

Exploring the Effectiveness of the Phase Features on Double Compressed AMR Speech Detection

Determining whether an audio signal is single compressed (SC) or double compressed (DC) is a crucial task in audio forensics, as it is closely linked to the integrity of the recording. In this paper, we propose the utilization of phase spectrum-based features for detecting DC narrowband and wideband adaptive multi-rate (AMR-NB and AMR-WB) speech. To the best of our knowledge, phase spectrum features have not been previously explored for DC audio detection. In addition to introducing phase spectrum features, we propose a novel parallel LSTM system that simultaneously learns the most representative features from both the magnitude and phase spectrum of the speech signal and integrates both sets of information to further enhance its performance. Analyses demonstrate significant differences between the phase spectra of SC and DC speech signals, suggesting their potential as representative features for DC AMR speech detection. The proposed phase spectrum features are found to perform as well as magnitude spectrum features for the AMR-NB codec, while outperforming the magnitude spectrum in detecting AMR-WB speech. The proposed phase spectrum features yield 8% performance improvement in terms of true positive rate over the magnitude spectrogram features. The proposed parallel LSTM system further improves DC AMR-WB speech detection.

Deep learning-based counter anti-forensic of GAN-based attack in HEVC compressed domain using coding pattern analysis

Double compression detection is one of the most popular techniques for the authentication of images and videos in the sphere of multimedia forensics. Several forensic methods have been suggested revealing video forgery, particularly high-efficiency video codec (HEVC) double compression detection. Even though forensic methods can effectively validate the integrity of videos, they become more intricate while practicing anti-forensic (AF) on manipulated videos. In this study, we present an innovative counter-anti-forensic (CAF) approach to detect anti-forensically misguided HEVC double compressed videos with the same coding parameters using coding pattern analysis. We mainly focus on employing generative adversarial networks (GANs) attacks to reconstruct artifacts that appear in compressed videos for deceiving forensic methods. In the proposed CAF method, we apply AF attacks twice in primary and secondary compressions and analyze the coding pattern changes to build a CAF method. We extract syntax information, and introduce new syntax map representations to improve distinguishability between single and double-compressed sequences. Additionally, we apply Gaussian and median filtering as AF attacks to show the robustness of the proposed method. The proposed deep learning-based CAF method shows better performance and can overwhelm state-of-the-art works.

CNN-based first quantization estimation of double compressed JPEG images

Multiple JPEG compressions leave artifacts in digital images: residual traces that could be exploited in forensics investigations to recover information about the device employed for acquisition or image editing software. In this paper, a novel First Quantization Estimation (FQE) algorithm based on convolutional neural networks (CNNs) is proposed. In particular, a solution based on an ensemble of CNNs was developed in conjunction with specific regularization strategies exploiting assumptions about neighboring element values of the quantization matrix to be inferred. Mostly designed to work in the aligned case, the solution was tested in challenging scenarios involving different input patch sizes, quantization matrices (both standard and custom) and datasets (i.e., RAISE and UCID collections). Comparisons with state-of-the-art solutions confirmed the effectiveness of the presented solution demonstrating for the first time to cover the widest combinations of parameters of double JPEG compressions.

A novel deep learning framework for double JPEG compression detection of small size blocks

Double JPEG compression detection plays a vital role in multimedia forensics, to find out whether a JPEG image is authentic or manipulated. However, it still remains to be a challenging task in the case when the quality factor of the first compression is much higher than that of the second compression, as well as in the case when the targeted image blocks are quite small. In this work, we present a novel end-to-end deep learning framework taking raw DCT coefficients as input to distinguish between single and double compressed images, which performs superior in the above two cases. Our proposed framework can be divided into two stages. In the first stage, we adopt an auxiliary DCT layer with sixty-four 8 × 8 DCT kernels. Using a specific layer to extract DCT coefficients instead of extracting them directly from JPEG bitstream allows our proposed framework to work even if the double compressed images are stored in spatial domain, e.g. in PGM, TIFF or other bitmap formats. The second stage is a deep neural network with multiple convolutional blocks to extract more effective features. We have conducted extensive experiments on three different image datasets. The experimental results demonstrate the superiority of our framework when compared with other state-of-the-art double JPEG compression detection methods either hand-crafted or learned using deep networks in the literature, especially in the two cases mentioned above. Furthermore, our proposed framework can detect triple and even multiple JPEG compressed images, which is scarce in the literature as far as we know.

Deep convolutional neural networks for double compressed AMR audio detection

Detection of double compressed (DC) adaptive multi-rate (AMR) audio recordings is a challenging audio forensic problem and has received great attention in recent years. Here, the authors propose to use convolutional neural networks (CNN) for DC AMR audio detection. The CNN is used as (i) an end-to-end DC AMR audio detection system and (ii) a feature extractor. The end-to-end system receives the audio spectrogram as the input and returns the decision whether the input audio is single compressed (SC) or DC. As a feature extractor in turn, it is used to extract discriminative features and then these features are modelled using support vector machines (SVM) classifier. Our extensive analysis conducted on four different datasets shows the success of the proposed system and provides new findings related to the problem. Firstly, double compression has a considerable impact on the high frequency components of the signal. Secondly, the proposed system yields great performance independent of the recording device or environment. Thirdly, when previously altered files are used in the experiments, 97.41% detection rate is obtained with the CNN system. Finally, the cross-dataset evaluation experiments show that the proposed system is very effective in case of a mismatch between training and test datasets.

Detection of AMR double compression using compressed-domain speech features

The adaptive multi-rate (AMR) audio codec is an established standard of speech signal compression, for either transmitting speech signal over mobile networks or storing digital audio on handheld devices. The widespread use of AMR codec and the high availability of tampering software have increased authentication cases in court. AMR double compression detection is a challenging engineering problem and a topic of multimedia forensics. As a general rule, a double compressed AMR file cannot be considered an original file. In this paper, a new method based on support vector machine (SVM) is proposed to classify single and double compressed AMR digital audio. Instead of using the decoded speech waveform, the proposed method uses only compressed-domain speech features. Specific parameters are extracted from encoded AMR files and used to create a set of statistical features. After applying robust scaling to features and selecting the SVM model, recursive feature elimination with correlation bias reduction (RFE-CBR) is used to determine the best number of features to maximize accuracy in SVM classification. The experiments reveal that the proposed algorithm can discriminate single and double compressed AMR speech, outperforming the published methods. The average accuracies using TIMIT database and CARIOCA1 database, the last recorded from landline phone calls, are about 99%. Other experiments, including frame offset attack and noise addition, found that the method is robust and reliable.

Video Integrity Verification and GOP Size Estimation Via Generalized Variation of Prediction Footprint

The Variation of Prediction Footprint (VPF), formerly used in video forensics for double compression detection and GOP size estimation, is comprehensively investigated to improve its acquisition capabilities and extend its use to video sequences that contain bi-directional frames (B-frames). By relying on a universal rate-distortion analysis applied to a generic double compression scheme, we first explain the rationale behind the presence of the VPF in double compressed videos and then justify the need of exploiting a new source of information such as the motion vectors, to enhance the VPF acquisition process. Finally, we describe the shifted VPF induced by the presence of B-frames and detail how to compensate the shift to avoid misguided GOP size estimations. The experimental results show that the proposed Generalized VPF (G-VPF) technique outperforms the state of the art, not only in terms of double compression detection and GOP size estimation, but also in reducing computational time.

DNN Based Moth Search Optimization for Video Forgery Detection

Recently, video tampering process becomes easier due to the rapid advancements in user-friendly editing software and multimedia technology (e.g., Mokey by Imagineer Systems, and Photoshop and Premiere by Adobe). This technologies may highly tamper the original videos, so that the audience gets mislead. Nowadays, MPEG-4 codec is included in a large proportions of video cameras and surveillance systems. Therefore the double compression detection process included as an initial step in the video forensic is receiving a high significance. In this paper, the double compression artifacts is detected by adopting the Markov based features, which identifies the interpolated original videos. The double compressed frames are then segmented by introducing an SLIC super pixel segmentation technique. Here, the feature extraction is performed by applying the scale information that is obtained from the multi-scale Gabor filters. The features of this Gabor scale accurately extract the structural features and also reduce too much of redundancy. This extracted features are then provided to DNN (deep neural network) for forgery detection. In this video forensic process, DNN classifier is included for forgery detection. The CNN classifier is included in various existing forgery detection techniques. But, in our work we include DNN because it contains number of hidden layers which provide accurate results for this forgery detection process. To improve the DNN performance, Moth Search Optimization (MSO) algorithm is introduced in this forgery detection technique. Every nook and corner of this world we can able to find the surveillance cameras for security purpose. But, some fraudsters perform forgeries in this recorded videos for their own benefits. To identify this, a lot of forgery detection techniques are coming into existence. So in this work, we introduce the DNN based MSO to perform the forgery detection in videos.This implementation is processed in python simulation platform. The parametric evaluations are taken in terms of F1-Score, average accuracy, Precision, Recall and. Experimental results will provide improved performance in video forgery detection.

AMR Compressed-Domain Analysis for Multimedia Forensics Double Compression Detection

An audio recording must be authentic to be admitted as evidence in a criminal prosecution so that the speech is saved with maximum fidelity and interpretation mistakes are prevented. AMR (adaptive multi-rate) encoder is a worldwide standard for speech compression and for GSM mobile network transmission, including 3G and 4G. In addition, such encoder is an audio file format standard with extension AMR which uses the same compression algorithm. Due to its extensive usage in mobile networks and high availability in modern smartphones, AMR format has been found in audio authenticity cases for forgery searching. Such exams compound the multimedia forensics field which consists of, among other techniques, double compression detection, i. e., to determine if a given AMR file was decompressed and compressed again. AMR double compression detection is a complex engineering problem whose solution is still underway. In general terms, if an AMR file is double compressed, it is not an original one and it was likely doctored. The published works in literature about double compression detection are based on decoded waveform AMR files to extract features. In this paper, a new approach is proposed to AMR double compression detection which, in spite of processing decoded audio, uses its encoded version to extract compressed-domain linear prediction (LP) coefficient-based features. By means of feature statistical analysis, it is possible to show that they can be used to achieve AMR double compression detection in an effective way, so that they can be considered a promising path to solve AMR double compression problem by artificial neural networks.

HEVC double compression detection under different bitrates based on TU partition type

During the process of authenticating the integrity of digital videos, double compression is an important evidence. High-efficiency video coding (HEVC) is the latest coding standard proposed in 2013 and has shown superior performance over its predecessors. It brings in several novel syntactic units, such as coding tree unit (CTU), prediction unit (PU), and transform unit (TU). Few methods have been reported to detect HEVC double compression by utilizing such new characteristics. In this paper, a novel scheme based on TU is proposed to detect double compression of HEVC videos. The histogram of each TU partition type in the first I/P frame of all GOPs is calculated. The feature set is fed into SVM to classify single and double compressed videos. Experimental results have demonstrated the effectiveness of TU-based feature (accuracy in [0.84, 0.97]) that bears very low dimension (10-D). And it is also shown that TU-based feature set can be combined with other feature sets to boost their performance.

Detecting double compression for JPEG images of low quality factor

Double JPEG compression is often used to conceal some illegal operations on the source image. Since there exist some traces when an image undergoes double JPEG compression, the detection of double JPEG compression can be used as a tool of digital forensics. In the past decade, researchers have focused on detecting double compression with the same quality factor (QF) but cannot provide reliable results when the QF is approximately below 70. To remedy this, we propose to analyze the low-frequency components and the error image and extract two types of features to improve the detection accuracy. Since the high-frequency components are easily lost in the JPEG compression, the low-frequency components are utilized to extract the reliable features. With this in mind, our method first analyzes the low-frequency discrete cosine transform coefficients and the lost information. Then, the features are extracted to characterize the difference between the two sequential compressions. Finally, the feature set is fed to a support vector machine for classification. Experimental results on two standard image databases verify that our method could improve the detection accuracy of the low QF double compressed JPEG images with the same QF. The average classification accuracy of the different QFs (the range of QF is usually set from 70 to 20) was obtained as 87.75%.

An Adaptive JPEG Double Compression Steganographic Scheme Based on Irregular DCT Coefficients Distribution

Steganography is to embed secret information into digital media and minimize the distortion caused by data embedding. The JPEG is a widely used image compression format, and many JPEG images transmitted on the network are double compressed. Most of the existing steganography algorithms mainly use the single compressed images as the embedded carrier of secret information while rarely taking advantage of the double compressed images. However, the double compressed operation usually leads to changes in the statistical characteristics of images. Therefore, the minor changes caused by steganography can be confused by double compression operation to achieve the purpose of concealing embedding operation. This paper proposes a secure JPEG double compression (DC) steganographic scheme based on irregular discrete cosine transformation (DCT) and coefficients distribution (IDCD-JDS), which can obtain less statistical detectability. The minimum distortion function is designed according to the fact that the statistical distribution of the double compressed images has periodic peaks and valleys as well as multiple irregular intervals. By using the syndrome trellis coding (STC) to embed secret information, the modifications are limited to regions that are difficult to detect. The quality factors (QF) relationship between the first and second compression is further studied to explore the effective range for double compressed images so that the proposed algorithm can achieve better performance under different conditions. The experiments show that this scheme can reduce the embedding change by utilizing the inherent statistical distribution and irregular block complexity in double compressed images, and it performs better than the existing methods.

Detection of Double Compressed HEVC Videos Using GOP-Based PU Type Statistics

Double compression detection is of great significance to recover the compression history of suspicious videos and help analyzers locate tampering points in forged videos. However, the lack of efficient methods still haunts researchers when it comes to double HEVC compression detection with a shifted group of pictures (GOP) structures. To tackle this problem, we first analyze the properties of re-encoded frames in double compressed HEVC videos. It is found that the abnormal statistics of prediction unit (PU) types in relocated I-frames can be used as the clue to expose double compression. Relocated I-frames are re-encoded P-frames, which were I-frames in the first compression. Different from the existing works, we designed the GOP-based features instead of frame-based features, enabling more efficient utilization of temporal variation patterns of PU-type statistics within each GOP unit. In the proposed method, the ratio of I-PUs and S-Pus is first computed to construct PU sequences for each GOP unit. The three-tap median filter is applied to PU sequences in order to mitigate the influence of various video contents. Several first-order statistics, including average, variation, and maximum/minimum, are selected to generate a 6-D detection feature. Then, multi-layer perceptron (MLP) is used to classify GOP-based features. Finally, the score fusion strategy is applied to obtain the detection result of the input video. Several publicly available YUV sequences are used to construct the double compression dataset with various coding parameters. In experiments, the proposed method outperforms several state-of-the-art methods under different settings of bitrates and GOP structures. The proposed method has more robust detection capability even when tampered videos are generated by combining two video clips with different GOP sizes.

Detection of double compression in HEVC videos based on TU size and quantised DCT coefficients

With the advent of sophisticated and low-cost video editing software, digital videos are highly vulnerable to be tampered. The authenticity and integrity identification of digital videos is an urgent issue. In this study, an effective method to detect double High Efficiency Video Coding (HEVC) video compression with different quantisation parameter (QP) is proposed, which often occurs in the video tampering process. First, the effects of QP on the distributions of Discrete Cosine Transform (DCT) coefficients and Transform Unit (TU) size are analysed. Then a feature set including 17 features is derived from quantised DCT coefficients and TU size. It can characterize the statistical differences between single and double compressed videos. Finally, the Library for Support Vector Machine classifier is exploited to identify whether a given HEVC video has been double compressed or not. Experimental results demonstrate that the authors' detection method has a good comprehensive performance.

Detection of double JPEG compression using modified DenseNet model

With the increasing tendency of the tempering of JPEG images, development of methods detecting image forgery is of great importance. In many cases, JPEG image forgery is usually accompanied with double JPEG compression, leaving no visual traces. In this paper, a modified version of DenseNet (densely connected convolutional networks) is proposed to accomplish the detection task of primary JPEG compression among double compressed images. A special filtering layer in the front of the network contains typically selected filtering kernels that can help the network following to discriminating the images more easily. As shown in the results, the network has achieved great improvement compared to the-state-of-the-art method especially on the classification accuracy among images with lower quality factors.

Detecting double compressed MPEG videos with the same quantization matrix and synchronized group of pictures structure

Double compression detection is a crucial stage in digital image and video forensics. However, the detection of double compressed videos is challenging when the video forger uses the same quantization matrix and synchronized group of pictures (GOP) structure during the recompression history to conceal tampering effects. A passive approach is proposed for detecting double compressed MPEG videos with the same quantization matrix and synchronized GOP structure. To devise the proposed algorithm, the effects of recompression on P frames are mathematically studied. Then, based on the obtained guidelines, a feature vector is proposed to detect double compressed frames on the GOP level. Subsequently, sparse representations of the feature vectors are used for dimensionality reduction and enrich the traces of recompression. Finally, a support vector machine classifier is employed to detect and localize double compression in temporal domain. The experimental results show that the proposed algorithm achieves the accuracy of more than 95%. In addition, the comparisons of the results of the proposed method with those of other methods reveal the efficiency of the proposed algorithm.

Detection of Double Compression With the Same Coding Parameters Based on Quality Degradation Mechanism Analysis

Detection of double compression with the same coding parameters is a very challenging problem in video forensics, since traces of recompression operations are extremely slight in this case. To solve this problem, we first analyze degradation mechanisms during recompression. It is observed that the video quality tends to become nearly unchanged after multiple recompressions with the same coding parameters. The degree of quality degradation is used to distinguish single and double compressed videos. This property can be described using the convergent tendency of video data to unchanged states after continuous recompressions. For MPEG videos, statistical features of rounding and truncation errors are extracted from the intra-coding process while macroblock-mode based features are obtained from the inter-coding process. The final feature is generated by concatenating these two sets of features to provide robust detection capability. Then, extracted features are fed to the SVM classifier to obtain the final detection result. In addition, aforementioned features are modified and extended to detect double compression on H.264 videos based on the unique coding techniques developed in the H.264 standard, such as intra-prediction. Several public available YUV sequences are used to construct double compression databases with three popular coding standards, including MPEG-2, MPEG-4, and H.264. In experiments, the proposed method outperforms several state-of-the-art methods for different compression qualities and rate control schemes. Experimental results demonstrate the proposed method has more robust detection capability of double compression under various encoding configurations.

Malicious inter-frame video tampering detection in MPEG videos using time and spatial domain analysis of quantization effects

In this paper, a new algorithm is proposed for forgery detection in MPEG videos using spatial and time domain analysis of quantization effect on DCT coefficients of I and residual errors of P frames. The proposed algorithm consists of three modules, including double compression detection, malicious tampering detection and decision fusion. Double compression detection module employs spatial domain analysis using first significant digit distribution of DCT coefficients in I frames to detect single and double compressed videos using an SVM classifier. Double compression does not necessarily imply the existence of malignant tampering in the video. Therefore, malicious tampering detection module utilizes time domain analysis of quantization effect on residual errors of P frames to identify malicious inter-frame forgery comprising frame insertion or deletion. Finally, decision fusion module is used to classify input videos into three categories, including single compressed videos, double compressed videos without malicious tampering and double compressed videos with malicious tampering. The experimental results and the comparison of the results of the proposed method with those of other methods show the efficiency of the proposed algorithm.

A novel forensic image analysis tool for discovering double JPEG compression clues

This paper presents a novel technique to discover double JPEG compression traces. Existing detectors only operate in a scenario that the image under investigation is explicitly available in JPEG format. Consequently, if quantization information of JPEG files is unknown, their performance dramatically degrades. Our method addresses both forensic scenarios which results in a fresh perceptual detection pipeline. We suggest a dimensionality reduction algorithm to visualize behaviors of a big database including various single and double compressed images. Based on intuitions of visualization, three bottom-up, top-down and combined top-down/bottom-up learning strategies are proposed. Our tool discriminates single compressed images from double counterparts, estimates the first quantization in double compression, and localizes tampered regions in a forgery examination. Extensive experiments on three databases demonstrate results are robust among different quality levels. F1-measure improvement to the best state-of-the-art approach reaches up to 26.32 %. An implementation of algorithms is available upon request to fellows.

Factor Histogram based Forgery Localization in Double Compressed JPEG Images

Today convincing digital forgery can be created without master learning of image editing software. These fake pictures over exceptionally quick media may cause extreme results in the public arena. Passive digital image forensic is an area which uncovers these problems. Since JPEG compression deals with 8 × 8 DCT matrix it makes its own fingerprint which can be utilized to distinguish further forgeries in the picture. In this paper, we have proposed a technique which automatically locates forgery in the image based on histogram of DCT coefficient factors, called as factor histogram. When image undergoes aligned double compression this factor histogram shows peak at current quantization step as well as primary quantization step. Our algorithm searches for absence of such double maxima in block-wise factor histogram to identify tampered region. This method can find copy-move, copy-paste as well as pre-processed forgeries such as rotation and scaling.