Showing papers on "Discrete cosine transform published in 2020"

Coverless real-time image information hiding based on image block matching and dense convolutional network

Abstract: Information security has become a key issue of public concern recently. In order to radically resist the decryption and analysis in the field of image information hiding and significantly improve the security of the secret information, a novel coverless information hiding approach based on deep learning is proposed in this paper. Deep learning can select the appropriate carrier according to requirements to achieve real-time image data hiding and the high-level semantic features extracted by CNN are more accurate than the low-level features. This method does not need to employ the designated image for embedding the secret data but transfer a set of real-time stego-images which share one or several visually similar blocks with the given secret image. In this approach, a group of real-time images searched online are segmented according to specific requirements. Then, the DenseNet is used to extract the high-level semantic features of each similar block. At the same time, a robust hash sequence with feature sequence, DC and location is generated by DCT. The inverted index structure based on the hash sequence is constructed to attain real-time image matching efficiently. At the sending end, the stego-images are matched and sent through feature retrieval. At the receiving end, the secret image can be recovered by extracting similar blocks through the received stego-images and stitching the image blocks according to the location information. Experimental results demonstrate that the proposed method without any modification traces provides better robustness and has higher retrieval accuracy and capacity when compared with some existing coverless image information hiding.

Implicit Dual-Domain Convolutional Network for Robust Color Image Compression Artifact Reduction

Abstract: Several dual-domain convolutional neural network-based methods show outstanding performance in reducing image compression artifacts. However, they are unable to handle color images as the compression processes for gray scale and color images are different. Moreover, these methods train a specific model for each compression quality, and they require multiple models to achieve different compression qualities. To address these problems, we proposed an implicit dual-domain convolutional network (IDCN) with a pixel position labeling map and quantization tables as inputs. We proposed an extractor-corrector framework-based dual-domain correction unit (DCU) as the basic component to formulate the IDCN; the implicit dual-domain translation allows the IDCN to handle color images with discrete cosine transform (DCT)-domain priors. A flexible version of IDCN (IDCN-f) was also developed to handle a wide range of compression qualities. Experiments for both objective and subjective evaluations on benchmark datasets show that IDCN is superior to the state-of-the-art methods and IDCN-f exhibits excellent abilities to handle a wide range of compression qualities with a little trade-off against performance; further, it demonstrates great potential for practical applications.

Color image watermarking based on orientation diversity and color complexity

Abstract: The Just Noticeable Distortion (JND) can reliably measure the perceptual strength in image watermarking, but, it remains a challenge to computationally model the process of embedding watermark without prior knowledge of the image contents. This paper proposed a novel color image watermarking scheme in discrete cosine transform (DCT) domain based on JND, which takes both orientation diversity and color complexity features into account. Firstly, two indicator was introduced which take into account the differences in the texture types and orientation diversity of the Human Visual System (HVS) in the proposed JND contrast masking (CM) processing. In addition, a novel color complexity weight from Cb-channel is used to guarantee the scheme robustness. Then, a novel JND model combined with the proposed contrast masking and color complexity is applied into quantization watermarking scheme. Compared with the state-of-the-art methods for color image watermarking, experimental results using publicly available images show that our proposed scheme is reliable and effective.

Significance of Subband Features for Synthetic Speech Detection

Abstract: In text-to-speech or voice conversion based synthetic speech detection, it is a common practice that spectral information over the entire frequency band is used for feature representation. We propose a new method, referred to as subband transform, that characterizes the signals by subband. It is found that subband transform captures the artifacts in synthetic speech more effectively than full band transform. We propose equal subband transform, octave subband transform, and mel subband transform for three novel features, namely, constant-Q equal subband transform (CQ-EST), constant-Q octave subband transform (CQ-OST) and discrete Fourier mel subband transform (DF-MST). We evaluate the three features on the ASVspoof 2015, noisy ASVspoof 2015 and ASVspoof 2019 logical access corpora. The experiments show that the proposed CQ-EST feature achieves an average equal error rate of 0.056% on ASVspoof 2015 evaluation set. The study observes that the features based on subband transform outperform those based on full band transform under both clean and noisy conditions. In addition, the tandem detection cost function of CQ-OST can reach 0.188 on ASVspoof 2019 logical access evaluation set.

A New High Capacity Image Steganography Method Combined With Image Elliptic Curve Cryptography and Deep Neural Network

Abstract: Image steganography is a technology that hides sensitive information into an image. The traditional image steganography method tends to securely embed secret information in the host image so that the payload capacity is almost ignored and the steganographic image quality needs to be improved for the Human Visual System(HVS). Therefore, in this work, we propose a new high capacity image steganography method based on deep learning. The Discrete Cosine Transform(DCT) is used to transform the secret image, and then the transformed image is encrypted by Elliptic Curve Cryptography(ECC) to improve the anti-detection property of the obtained image. To improve steganographic capacity, the SegNet Deep Neural Network with a set of Hiding and Extraction networks enables steganography and extraction of full-size images. The experimental results show that the method can effectively allocate each pixel in the image so that the relative capacity of steganography reaches 1. Besides, the image obtained using this steganography method has higher Peak Signal-to-Noise Ratio(PSNR) and Structural Similarity Index(SSIM) values, reaching 40dB and 0.96, respectively.

Comparison of Different Precoding Techniques for Unbalanced Impairments Compensation in Short-Reach DMT Transmission Systems

Abstract: Channel independent precoding technique has been widely used in optical discrete multi-tone (DMT) transmission systems to compensate unbalanced impairments induced by bandwidth limitations and imperfect frequency responses of electrical/optical devices and various interferences. However, the comparison of different precoding techniques in terms of peak-to-average power ratio (PAPR) reduction, nonlinear distortion tolerance, implementation complexity, and bit error rate (BER) improvements has not been fully studied. In this article, we comparatively investigate seven most commonly used precoding techniques, i.e., discrete Fourier transform (DFT), orthogonal circulant matrix transform (OCT), constant amplitude zero autocorrelation sequence-based matrix transform (CAZACT), Zadoff-Chu matrix transform (ZCT), discrete cosine transform (DCT), discrete Hartley transform (DHT), and Walsh-Hadamard transform (WHT), through both numerical simulations and offline experiments. Simulations show that the ZCT can achieve the best PAPR reduction, and the OCT cannot reduce the PAPR. Besides, DFT, CAZACT, ZCT, DCT, and DHT precoded DMT signals have superior error vector magnitude performance after passing through nonlinear models. And the corresponding precoded QPSK-DMT signals have better BER performance than both OCT/WHT precoded and conventional ones in the distortion-limited scenarios. However, the precoded 16/64QAM-DMT signals, excluding OCT precoded one, are more sensitive to nonlinear distortions and provide minor BER improvement or even may degrade the BER performance. Complexity analysis exhibits the WHT precoding does not require multiplications and therefore has the lowest implementation complexity. In the inter-symbol interference-limited case, OCT precoding can still achieve a good signal-to-noise ratio (SNR) balance and provide the best BER performance. A simple timing synchronization point adjustment (TSPA) method is employed to enhance SNR balance. Simulated and experimental results exhibit that similar BER performance can be achieved by using these precoding techniques together with TSPA in noise-limited scenarios. Considering the implementation complexity, WHT precoding may be a good option to compensate unbalanced impairments in the short-reach DMT transmission system.

Unsupervised band selection based on weighted information entropy and 3D discrete cosine transform for hyperspectral image classification

Abstract: Band selection is an effective means of reducing the dimensionality of the hyperspectral image by selecting the most informative and distinctive bands. Bands are usually selected by adoptin...

Multiple Robustness Enhancements for Image Adaptive Steganography in Lossy Channels

Abstract: Considering that traditional image steganography technologies suffer from the potential risk of failure under lossy channels, an enhanced adaptive steganography with multiple robustness against image processing attacks is proposed, while maintaining good detection resistance. First, a robust domain constructing method is proposed utilizing robust element extraction and optimal element modification, which can be applied to both spatial and JPEG images. Then, a robust steganography is proposed based on “Robust Domain Constructing + RS-STC Codes,” combined with cover selection, robust cover extraction, message coding, and embedding with minimized costs. In addition, to provide a theoretical basis for message extraction integrity, the fault tolerance of the proposed algorithm is deduced using error model based on burst errors and decoding damage. Finally, on the basis of parameter discussion about robust domain construction, performance experiments are conducted, and the recommended coding parameters are given for lossy channels with different attacks using the analytic results for fault tolerance. A series of experimental results demonstrate that the proposed algorithm can extract embedded messages with significantly higher accuracy after different attacks, such as compression, noising, scaling and other attacks, compared with the state-of-the-art adaptive steganography, and robust watermarking algorithms, while maintaining good detection resistant performance.

Image encryption based on compressive sensing and chaos systems

Abstract: Recently, a new image encryption technique based on compressive sensing (CS) has been proposed. CS allows the signal to be sampled at a much lower rate than the Nyquist- Shannon rate. Furthermore, the signal can be sampled and compressed in a single step using the sparsity of the signal which represents the signal with a reduced number of samples. The signal can be sparse in its original domain or in another domain such as Discrete Cosine Transform (DCT), Discrete Fourier Transform (DFT) or Discrete Wavelet Transform (DWT). In addition, CS can be used for encryption by using a good measurement matrix as the secret key of the encryption algorithm, so that CS samples, compresses and encrypts in a one-step process. This paper introduces a new algorithm based on CS for image encryption. Plain image is sparse in the DCT domain, and the combination of pixels is used to reduce the dimension of the image DCT. The measurement matrix is generated using a three-dimensional chaotic system, and for further encryption, pixel scrambling is applied using a one-dimensional chaotic system to generate the scrambling vector. Experimental and analysis results show that the proposed algorithm has good performance in terms of security and image compression, as well as low time complexity.

An Integer Approximation Method for Discrete Sinusoidal Transforms

Abstract: Approximate methods have been considered as a means to the evaluation of discrete transforms. In this work, we propose and analyze a class of integer transforms for the discrete Fourier, Hartley, and cosine transforms (DFT, DHT, and DCT), based on simple dyadic rational approximation methods. The introduced method is general, applicable to several block-lengths, whereas existing approaches are usually dedicated to specific transform sizes. The suggested approximate transforms enjoy low multiplicative complexity and the orthogonality property is achievable via matrix polar decomposition. We show that the obtained transforms are competitive with archived methods in literature. New 8-point square wave approximate transforms for the DFT, DHT, and DCT are also introduced as particular cases of the introduced methodology.

Multi-dimensional particle swarm optimization for robust blind image watermarking using intertwining logistic map and hybrid domain

Abstract: Robust blind watermarking has become a vital means of copyright protection, and this paper presents a new optimal robust and blind watermarking method of grayscale images based on intertwining logistic map and a variant of particle swarm optimization (PSO) in a hybrid domain. In the proposed approach, firstly a host image is decomposed by discrete wavelet transform, and discrete cosine transform (DCT) is applied to insensitive LH and HL subbands according to human visual model. Then, optimum frequency spectra in the DCT domain are chosen to form a feature matrix for improving the robustness and transparency of watermark. Finally, a shuffled watermark image using the chaotic logistic map is inserted by modifying the largest singular values of a feature matrix pair in the singular value decomposition domain. An improved version of PSO is employed to perform multi-dimensional optimization for selection of the most qualified DCT coefficients and estimation of watermark embedding strength in terms of their significant influence on imperceptibility and robustness. The security of the proposed method is provided by intertwining logistic map. Experimental results on standard test images indicate that PSO searches efficiently optimal values of watermark embedding strength and the most suitable DCT subbands, and the proposed watermarking algorithm performs much better than the other compared schemes in imperceptibility and robustness objectives.

Discrete Transforms and Matrix Rotation Based Cancelable Face and Fingerprint Recognition for Biometric Security Applications.

Abstract: The security of information is necessary for the success of any system. So, there is a need to have a robust mechanism to ensure the verification of any person before allowing him to access the stored data. So, for purposes of increasing the security level and privacy of users against attacks, cancelable biometrics can be utilized. The principal objective of cancelable biometrics is to generate new distorted biometric templates to be stored in biometric databases instead of the original ones. This paper presents effective methods based on different discrete transforms, such as Discrete Fourier Transform (DFT), Fractional Fourier Transform (FrFT), Discrete Cosine Transform (DCT), and Discrete Wavelet Transform (DWT), in addition to matrix rotation to generate cancelable biometric templates, in order to meet revocability and prevent the restoration of the original templates from the generated cancelable ones. Rotated versions of the images are generated in either spatial or transform domains and added together to eliminate the ability to recover the original biometric templates. The cancelability performance is evaluated and tested through extensive simulation results for all proposed methods on a different face and fingerprint datasets. Low Equal Error Rate (EER) values with high AROC values reflect the efficiency of the proposed methods, especially those dependent on DCT and DFrFT. Moreover, a comparative study is performed to evaluate the proposed method with all transformations to select the best one from the security perspective. Furthermore, a comparative analysis is carried out to test the performance of the proposed schemes with the existing schemes. The obtained outcomes reveal the efficiency of the proposed cancelable biometric schemes by introducing an average AROC of 0.998, EER of 0.0023, FAR of 0.008, and FRR of 0.003.

A robust copy-move forgery detection technique based on discrete cosine transform and cellular automata

Abstract: Copy Move Forgery (CMF) is a type of digital image forgery in which an image region is copied and pasted to another location within the same image with malicious intent to misrepresent its meaning. To prevent misinterpretation of an image content, several Copy Move Forgery Detection (CMFD) methods have been proposed in the past. However, the existing methods show limited robustness on images altered with post-processing attacks such as noise addition, compression, blurring etc. In this paper, we propose a robust method for detecting copy-move forgeries under different post-processing attacks. We use Discrete Cosine Transform (DCT) to extract features from each block. Next, Cellular Automata is employed to construct feature vectors based on the sign information of the DCT coefficients. Finally, feature vectors are matched using the kd-tree based nearest-neighbor searching method to find the duplicated areas in the image. Experimental results show that the proposed method performs exceptionally well relative to the other state-of-the-art methods from the literature even when an image is heavily affected by the post-processing attacks, in particular, JPEG compression and additive white Gaussian noise. Furthermore, experiments confirm the robustness of the proposed method against the range of combined attacks.

Improved wavelet-based image watermarking through SPIHT

Abstract: This paper presents an improved watermarking algorithm using discrete wavelet transform (DWT), discrete cosine transforms (DCT) and singular value decomposition (SVD). Further, robustness and security of algorithm is enhanced by set partitioning in hierarchical tree (SPIHT) and Arnold transform, respectively. The experimental results evident that proposed method is imperceptible and robust against various form of attacks and found superior to other similar technique under consideration.

Steganography by Minimizing Statistical Detectability: The cases of JPEG and Color Images

Abstract: This short paper presents a novel method for steganography in JPEG-compressed images, extended the so-called MiPOD scheme based on minimizing the detection accuracy of the most-powerful test using a Gaussian model of independent DCT coefficients This method is also applied to address the problem of embedding into color JPEG images The main issue in such case is that color channels are not processed in the same way and, hence, a statistically based approach is expected to bring significant improvements when one needs to consider heterogeneous channels together The results presented show that, on the one hand, the extension of MiPOD for JPEG domain, referred to as J-MiPOD, is very competitive as compared to current state-of-the-art embedding schemes On the other hands, we also show that addressing the problem of embedding in JPEG color images is far from being straightforward and that future works are required to understand better how to deal with color channels in JPEG images

An improved robust image watermarking by using different embedding strengths

Abstract: Image watermarking technique is an alternative solution to protecting digital image copyright. This paper proposed a new embedding technique based on different embedding strengths for embedding a watermark. An image is divided into non-overlapping blocks of 8 × 8 pixels. The variance pixel value was computed for each image block. Image blocks with the highest variance value were selected for the embedding regions. Therefore, it was transformed by discrete cosine transforms (DCT). Five DCT coefficients in the middle frequency were selected and the average of selected DCT blocks was calculated to generate different embedding strengths by using a set of rules. The watermark bits were embedded by using a set of embedding rules with the proposed different embedding strengths. For an additional security, the binary watermark was scrambled by using an Arnold Transform before it was embedded. The experimental results showed that the proposed scheme achieved a higher imperceptibility than the other existing schemes. The proposed scheme achieved a watermarked image quality with a PSNR value of 46 dB. The proposed scheme also produced a high watermark extracting resistance under various attacks.

Recompression of JPEG Crypto-Compressed Images Without a Key

Abstract: The rising popularity of social networks and cloud computing has greatly increased a number of JPEG compressed image exchanges. In this context, the security of the transmission channel and/or the cloud storage can be susceptible to privacy leaks. The selective encryption is an efficient tool to mask the image content and to protect confidentiality while remaining format-compliant. However, image processing in the encrypted domain is not a trivial task. In this paper, we present a JPEG crypto-compression method which allows us to recompress a JPEG crypto-compressed image several times, without any information about the secret key or the original image content. Indeed, using the proposed method in this paper, each recompression can be done directly on the JPEG bitstream by removing the last bit of the code representation of each non-zero coefficient, adapting the entropic code part, and slightly modifying the quantization table. This method is efficient to recompress JPEG crypto-compressed images in terms of compression ratio. Moreover, the decryption of the recompressed image produces an image with a very similar visual quality when compared to the original image, according to the obtained results.

Diversity-Based Cascade Filters for JPEG Steganalysis

Abstract: Steganalysis is a technique for detecting the existence of secret information hidden in digital media. In this paper, we propose a novel scheme for JPEG steganalysis. In this scheme, we first design the diverse base filters which are able to obtain the image residuals from various directions. Then, we propose a cascade filter generation strategy to construct a set of high order cascade filters from the base filters. We further select the cascade filters with the maximum diversity. The selected filters are convolved with the decompressed JPEG image to obtain residuals which capture the subtle embedding traces. The residuals, termed as the maximum diversity cascade filter residual, are eventually used to extract features to train an ensemble classifier for classification. The experiments are carried out on the detection of stego-images generated using common JPEG steganographic schemes, the results of which demonstrate the effectiveness of the proposed scheme for JPEG steganalysis.

Reverse JPEG Compatibility Attack

Abstract: A novel steganalysis method for JPEG images is introduced that is universal in the sense that it reliably detects any type of steganography as well as small payloads. It is limited to quality factors 99 and 100. The detection statistic is formed from the rounding errors in the spatial domain after decompressing the JPEG image. The attack works whenever, during compression, the discrete cosine transform is applied to integer-valued signal. Reminiscent of the well-established JPEG compatibility steganalysis, we call the new approach the “reverse JPEG compatibility attack.” While the attack is introduced and analyzed under simplifying assumptions using reasoning based on statistical signal detection, the best detection in practice is obtained with machine learning tools. Experiments on diverse datasets of both grayscale and color images, five steganographic schemes, and with a variety of JPEG compressors demonstrate the universality and applicability of this steganalysis method in practice.

Feature-Guided SAR-to-Optical Image Translation

Abstract: The powerful performance of Generative Adversarial Networks (GANs) in image-to-image translation has been well demonstrated in recent years. However, most methods are focused on completing an isolated image translation task. With the complex scenes in optical images and high-frequency speckle noise in SAR images, the quality of generated images is often unsatisfactory. In this paper, a feature-guided method for SAR-to-optical image translation is proposed to better take the unique attributes of images into account. Specifically, in view of the diversity of structure features and texture features, VGG-19 network is used as the feature extractor in the task of cross-modal image translation. To ensure the acquisition of multilayer features in the process of image generation, feature matching is carried out on different layers. Loss function based on Discrete Cosine Transform is designed to filter out the high-frequency noise. The generated images show better performance in feature preservation and noise reduction, and achieve higher Image Quality Assessment scores compared with images generated by some famous methods. The superiority of our algorithm is also demonstrated by being applied to different networks.

A Deep Learning Approach in the Discrete Cosine Transform Domain to Median Filtering Forensics

Abstract: This letter presents a novel median filtering forensics approach, based on a convolutional neural network (CNN) with an adaptive filtering layer (AFL), which is built in the discrete cosine transform (DCT) domain. Using the proposed AFL, the CNN can determine the main frequency range closely related with the operational traces. Then, to automatically learn the multi-scale manipulation features, a multi-scale convolutional block is developed, exploring a new multi-scale feature fusion strategy based on the maxout function. The resultant features are further processed by a convolutional stream with pooling and batch normalization operations, and finally fed into the classification layer with the Softmax function. Experimental results show that our proposed approach is able to accurately detect the median filtering manipulation and outperforms the state-of-the-art schemes, especially in the scenarios of low image resolution and serious compression loss.

Efficient HEVC Integrity Verification Scheme for Multimedia Cybersecurity Applications

Abstract: Multimedia cybersecurity is a prevalent research topic in the digital world due to the rapid progress of digital multimedia and Internet applications. Watermarking, encryption, and steganography schemes are employed to attain multimedia data confidentiality and robustness. However, these schemes are externally applied on trusted computers, and there has been a lack of similar schemes that can be effectively and efficiently enabled through an untrusted transmission medium. In this work, a self-embedding-based High-Efficiency Video Coding (HEVC) transmission and integrity verification framework is presented. This framework is robust and reliable for verifying the integrity of HEVC frames transmitted through insecure communication channels. Firstly, the transmitted HEVC frames are divided into a number of blocks with a certain block size. After that, a discrete transform is used for self-embedding of watermarks from each block into another one depending on a predefined mechanism. The Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Discrete Fourier Transform (DFT) are tested for this task. The watermarked HEVC frames are transmitted through a wireless communication channel, and hence they become subject to different attacks and corruptions. At the receiver side, the secret watermarks in each block are sensed with a correlation-based method to discover dubious counterfeit operations. To verify the reliability of the suggested transmission framework and its ability to achieve high protection and robust content verification of the transmitted HEVC frames over insecure communication channels, different HEVC analyses and comparisons are performed. Simulation results demonstrate the suitability of the suggested transmission framework for different multimedia cybersecurity applications. Furthermore, the comparative analysis shows that the DFT is an efficient discrete transform that can be employed with the proposed transmission framework to guarantee a higher HEVC frame integrity. It allows higher sensitivity to simple modifications in the transmitted watermarked HEVC frames. This makes the suggested cybersecurity framework applicable, secure, and appropriate for multimedia integrity verification purposes.

Forward-Inverse 2D Hardware Implementation of Approximate Transform Core for the VVC Standard

Abstract: The future video coding standard named Versatile Video Coding (VVC) is expected by the end of 2020. VVC will enable better coding efficiency than the current High Efficiency Video Coding (HEVC) standard. This coding gain is brought by several coding tools. The Multiple Transform Selection (MTS) is one of the key coding tools that have been introduced in VVC. The MTS concept relies on three transform types including Discrete Cosine Transform (DCT)-II, Discrete Sine Transform (DST)-VII and DCT-VIII. Unlike the DCT-II that has fast computing algorithms, the DST-VII and DCT-VIII rely on more complex matrix multiplication. In this paper an approximation approach is proposed to reduce the computational cost of the DST-VII and DCT-VIII. The approximation consists in applying adjustment stages, based on sparse block-band matrices, to a variant of DCT-II family mainly DCT-II and its inverse. Genetic algorithm is used to derive the optimal coefficients of the adjustment matrices. Moreover, an efficient hardware implementation of the forward and inverse approximate transform module is proposed. The architecture design includes a pipelined and reconfigurable forward-inverse DCT-II core transform as it is the main core for DST-VII and DCT-VIII computations. The proposed 32-point 1D architecture including low cost adjustment stages allows the processing of a video in 2K and 4K resolutions at 1095 and 273 frames per second, respectively. A unified 2D implementation of forward-inverse DCT-II, approximate DST-VII and DCT-VIII is also presented. The synthesis results show that the design is able to sustain a video in 2K and 4K resolutions at 386 and 96 frames per second, respectively, while using only 12% of Alms, 22% of registers and 30% of DSP blocks of the Arria10 SoC platform.