A high-rate chaotic secured covert channel based on multiple embedding in images

Image watermarking techniques have continuously evolved to address new challenges and incorporate advanced features. The advent of data-driven approaches has enabled the processing and analysis of large volumes of data, extracting valuable insights and patterns. In this paper, we propose two content-aware quantization index modulation (QIM) algorithms: Content-Aware QIM (CA-QIM) and Content-Aware Minimum Distortion QIM (CAMD-QIM). These algorithms aim to improve the embedding distortion of QIM-based watermarking schemes by considering the statistics of the cover signal vectors and messages. CA-QIM introduces a canonical labeling approach, where the closest coset to each cover vector is determined during the embedding process. An adjacency matrix is constructed to capture the relationships between the cover vectors and messages. CAMD-QIM extends the concept of minimum distortion (MD) principle to content-aware QIM. Instead of quantizing the carriers to lattice points, CAMD-QIM quantizes them to close points in the correct decoding region. Canonical labeling is also employed in CAMD-QIM to enhance its performance. Both schemes can be categorized as (key-aided) semi-blind watermarking. Simulation results demonstrate the effectiveness of CA-QIM and CAMD-QIM in reducing embedding distortion compared to traditional QIM. The combination of canonical labeling and the minimum distortion principle proves to be powerful, minimizing the need for changes to most cover vectors/carriers. These content-aware QIM algorithms provide improved performance and robustness for watermarking applications.

Quantization index modulation (QIM) principle has shown superior performance in digital watermarking over spread-spectrum and low-bit(s) modulation based methods. In QIM based data hiding scheme, it is a challenging task to embed multiple bits of information into the same set of host signal samples. This work attempts to employ M-ary modulation for multibit QIM watermarking. The watermark embedding process may be divided into two phases. In the first phase, a binary watermark image is spatially dispersed using sequence of number generated by a secret key. In the second phase, host image is decomposed by integer wavelets and the encoded watermark bits are embedded into high-low (HL) and low-high (LH) subbands of DWT-coefficients using M-ary modulation. M-ary modulation is accomplished using near-orthogonal dither generated from the combination of random key, Hadamard matrix and step size. Simulation results show that robustness is increased, at the cost of increase in decoding complexity for high M-value.

Methods of attack on watermarks embedded with quantization index modulation and block DCT are introduced in this paper. The proposed method can detect the presence of hidden data by analyzing the histogram or spectrum of the transform coefficients, and prevent their extraction without further degrading the image. It is shown that, in some cases, the embedding-induced distortion can even be reduced in terms of peak-signal-to-noise ratio. Experimental results are given to show the effectiveness of the method.

Spread Transform Dither Modulation (STDM), a special case of Quantization Index Modulation (QIM), has been widely used in digital watermarking. STDM has good performance in robustness against re-quantization and random noise attacks, but it is largely vulnerable to the Fixed Gain Attack (FGA). In addition to digital images and videos watermarking applications, copyright protection for digital text such as Portable Document Format (PDF) has received particular attention and interest. In this paper, we modify the STDM watermarking scheme by making the quantization step dependent on the original samples during the embedding process and on the watermarked samples during the decoding process to resist the FGA attack and enhance the robustness against the Additive White Gaussian Noise (AWGN) attack and JPEG compression attack in both the spatial domain and frequency domain regardless of the source of elements used as cover work. Experimentations have been conducted distinctly on digital images and text PDF documents. The tested images were watermarked with a uniform fidelity, where SSIM is fixed to 0.982 and 0.953. Our approach achieves significant robustness against the FGA attack with an improvement of 98% in terms of Bit Error Rates (BER) compared to traditional STDM. As for the AWGN attack, an improvement of 21% is shown. The proposed method also presents robustness against a variety of filtering and geometric attacks, while preserving a high level of transparency.

A blind watermarking algorithm based on specific component quantization in DHT domain

This paper proposes a new approach for image data hiding that aims to improve both robustness and imperceptibility. The proposed approach is based on a texture-adaptive quantization index modulation (QIM) method that takes into account the local characteristics of the image texture to optimize the embedding process. The approach is evaluated using various image datasets and compared to existing state-of-the-art techniques. The results show that the proposed approach achieves better performance in terms of robustness against various image processing attacks, with BERs of zero in some attacks, while maintaining a high level of imperceptibility with PSNRs exceeding 46 decibels. It can be deduced that adaptation and normalization of the embedding strength arguments can enhance the QIM methods’ performance. The proposed approach has potential applications in image authentication, copyright protection, and data hiding.

As the number of IoT devices continue to exponentially increase and saturate the wireless spectrum, there is a dire need for additional spectrum to support large networks of wireless devices. Over the past years, many promising solutions have been proposed but they all suffer from the drawback of new infrastructure costs, setup and maintenance, or are difficult to implement due to FCC regulations. In this paper, we propose a novel Wireless Quantization Index Modulation (QIM) technique which uses existing infrastructure to embed information into existing wireless signals to communicate with IoT devices with negligible impact on the original signal and zero spectrum overhead. We explore the design space for wireless QIM and evaluate the performance of embedding information in TV, FM and AM radio broadcast signals under different conditions. We demonstrate that we can embed messages at up to 8-200 kbps with negligible impact on the audio and video quality of the original FM, AM and TV signals respectively.

In this paper, an artificial intelligent technique for robust digital image watermarking in multiwavelet domain is proposed. The embedding technique is based on the quantization index modulation technique and the watermark extraction process does not require the original image. We have developed an optimization technique using the genetic algorithms to search for optimal quantization steps to improve the quality of watermarked image and robustness of the watermark. In addition, we construct a prediction model based on image moments and back propagation neural network to correct an attacked image geometrically before the watermark extraction process begins. The experimental results show that the proposed watermarking algorithm yields watermarked image with good imperceptibility and very robust watermark against various image processing attacks.

In this paper, we present a new robust database watermarking scheme the originality of which stands on a semantic control of the data distortion and on the extension of quantization index modulation (QIM) to circular histograms of numerical attributes. The semantic distortion control of the embedding process we propose relies on the identification of existing semantic links in between values of attributes in a tuple by means of an ontology. By doing so, we avoid incoherent or very rare record occurrences which may bias data interpretation or betray the presence of the watermark. In a second time, we adapt QIM to database watermarking. Watermark embedding is conducted by modulating the relative angular position of the circular histogram center of mass of one numerical attribute. We theoretically demonstrate the robustness performance of our scheme against most common attacks (i.e., tuple insertion and deletion). This makes it suitable for copyright protection, owner identification, or traitor tracing purposes. We further verify experimentally these theoretical limits within the framework of a medical database of more than one half million of inpatient hospital stay records. Under the assumption imposed by the central limit theorem, experimental results fit the theory. We also compare our approach with two efficient schemes so as to prove its benefits.

Media (image, text, sound, and video) becomes available in the digital form. The ability to copy or modify contents and access to these media easily lead to making these problems challenging when transferring them to the internet. Therefore, data hiding methods are developed to handle these problems. In addition, cryptography algorithms can be combined with data hiding to increase the security of secret data that are used during the hiding process. In this paper, a new method of image steganography is proposed. The suggested approach is based on using ACM and array of random bits generated using BBS CSPRBG to encrypt the secret logo that is used during the hiding process. In embedding process, the cover-image is divided into blocks and the entropy of each block is computed. Then, the block that has a maximum value of entropy is selected to hide secret logo in it using (1st-LSB) algorithm. According to experimental results, the suggested method has achieved the best quality for the stego-image. Also, the secrecy of the logo is enhanced.

Recent developments in the speed of the Internet and information technology have made the rapid exchange of multimedia information possible. However, these developments in technology lead to violations of information security and private information. Digital steganography provides the ability to protect private information that has become essential in the current Internet age. Among all digital media, digital video has become of interest to many researchers due to its high capacity for hiding sensitive data. Numerous video steganography methods have recently been proposed to prevent secret data from being stolen. Nevertheless, these methods have multiple issues related to visual imperceptibly, robustness, and embedding capacity. To tackle these issues, this paper proposes a new approach to video steganography based on the corner point principle and LSBs algorithm. The proposed method first uses Shi-Tomasi algorithm to detect regions of corner points within the cover video frames. Then, it uses 4-LSBs algorithm to hide confidential data inside the identified corner points. Besides, before the embedding process, the proposed method encrypts confidential data using Arnold's cat map method to boost the security level. Experimental results revealed that the proposed method is highly secure and highly invisible, in addition to its satisfactory robustness against Salt & Pepper noise, Speckle noise, and Gaussian noise attacks, which has an average Structural Similarity Index (SSIM) of more than 0.81. Moreover, the results showed that the proposed method outperforms state-of-the-art methods in terms of visual imperceptibility, which offers excellent peak signal-to-noise ratio (PSNR) of average 60.7 dB, maintaining excellent embedding capacity.

Dense retrieval, which describes the use of contextualised language models such as BERT to identify documents from a collection by leveraging approximate nearest neighbour (ANN) techniques, has been increasing in popularity. Two families of approaches have emerged, depending on whether documents and queries are represented by single or multiple embeddings. ColBERT, the exemplar of the latter, uses an ANN index and approximate scores to identify a set of candidate documents for each query embedding, which are then re-ranked using accurate document representations. In this manner, a large number of documents can be retrieved for each query, hindering the efficiency of the approach. In this work, we investigate the use of ANN scores for ranking the candidate documents, in order to decrease the number of candidate documents being fully scored. Experiments conducted on the MSMARCO passage ranking corpus demonstrate that, by cutting of the candidate set by using the approximate scores to only 200 documents, we can still obtain an effective ranking without statistically significant differences in effectiveness, and resulting in a 2x speedup in efficiency.

Steganography is a method of hiding sensitive information within an ordinary file to avoid detection. When the file is received, the hidden information is extracted from the original file. Various types of files can be used to conceal the information, including images, audio files, video files, and text files. This research paper focuses on the use of image files as the medium. To create a stego-image, a secret image is embedded into a cover image. Both the cover and secret images are subjected to a Daubechies 3 Discrete Wavelet Transformation (DWT), then encrypted using Arnold's Cat Map algorithm and fused using a simple alpha blending technique with the cover image. DWT enhances the invisibility of hidden information, making it more challenging to detect by visual inspection or statistical analysis. Arnold’s cat map adds an extra layer of security, making it more difficult for an attacker to extract the message. Alpha blending helps to maintain the visual quality and integrity of the cover image, reducing the chances of noticeable artifacts or anomalies that may arise due to the embedding process. The resulting stego-image appears visually like the original cover image, making it less suspicious to potential attackers. Experimental results have been made on multiple area such as the best value of alpha, which DWT family to use, and different combinations of images as cover and payload images. The results demonstrate that our proposed hybrid model achieves a high level of security, robustness, and imperceptibility in image steganography. Moreover, it outperforms existing methods in terms of image quality metrics, such as achieving a Peak Signal-to-Noise Ratio (PSNR) value of up to 70.8 and a Structural Similarity Index (SSIM) value of up to 0.98982. These metrics indicate that the stego-image retains a remarkable level of visual fidelity and similarity to the cover image, thereby enhancing the overall effectiveness of the steganography technique.

Compared with traditional methods, word em-bedding is an efficient language representation that can learn syntax and semantics by using neural networks. As the result, more and more promising experiments in natural language processing (NLP) get the state-of-the-art results by introducing word embedding. In principle, embedding representation learning embeds words to a low-dimensional vector space, there-fore vectors support initialization of NLP tasks such as text classification, sentiment analysis, language understanding, etc. However, polysemy is very common in many languages, which causes word ambiguation, further influences the accuracy of the system. Additionally, language models based on distributed hypotheses mostly focused on word properties rather than morphology were our primary focus. This leads to unreasonable performance in different evaluations. At the same time, word embedding learning and measuring are two vital components of word representation. In this paper, we overviewed many language models including single sense and multiple sense word embedding, and many evaluated approaches including intrinsic and extrinsic evaluation. We found that there are obvious gaps between vectors and manual annotations in word similarity evaluation, and language models that achieved good performance in intrinsic evaluations could not produce similar results in extrinsic evaluations. To the best of our knowledge, there is no universal language model and embedding learning method for most NLP task, and each evaluations also hidden natural defects compared to human knowledge. More evaluated datasets are also investigated such as datasets used in intrinsic and extrinsic evaluations. We believe that an improved evaluation dataset and a more rational evaluation method would benefit from this overview.

A scheme is proposed to hide data in images based on a prioritised ordering of the content of the host image. The embedding process uses the watermark strength to determine the ordering of the 16 regions resulting from the second level wavelet transform DWT decomposed content of the host image. The DWT decomposed sub-bands of the cover image were analysed, transformed using the Pixel Value Difference (PVD), and then ranked in terms of their ability to withstand changes that do not imperil the visual quality (PSNR) of the watermarked image depending on which an N×N-sized watermark stream is hidden in the 8 highest ranked sub-bands. Using the proposal, a 256×256 Lena image had the capacity to accommodate up to 96800 bits of a watermark logo while still maintaining an appreciable visual quality manifested by a PSNR value of 33.66 dB.