A Design of Multi-Purpose Image-Based QR Code

A multi-purpose image-based QR code is designed in this paper. There are four purposes for the generated image-based QR code. In the first purpose, the basic image-based QR code with the look of a host image is with an ingenious layout to be identified easier. In the second one, a saliency region detection method is adopted for enhancing the quality of the image-based QR code. In the third one, the host image is embedded into the image-based QR code for further access to the host image; Finally, the visual cryptography-based watermarking method is applied to the host image embedded image-based QR code. In the case that the specific users need verification from the image-based QR code, the binary verified image can be retrieved when the public share is available. The experimental results demonstrate that the generated image-based QR code not only looked better than some previous works but also had high quality host image embedded and identification ability.

A Review on Watermarking using Local Binary Pattern

Local binary patterns are best known because of their robust texture-defining capacities and digital watermarking used to prove multimedia content copyright. This work presents an overview of the binary watermark in the image blocks by changing the pixels conveyed by the LBP pattern of the neighborhood. However, different photo blocks can have the same LBP pattern, which in the watermark process can lead to incorrect detection. In other words, without changing your watermark message, one can change the host image deliberately. Moreover, before watermark embedding, there is no encryption procedure, which leads to another potential security problem. In this paper, we examine the identical process of LBP synthesis or reverse LBP and its suitability for the digital watermarking image. The process of LBP synthesis varies by pixel values so that the LBP from these pixels is the required synthesizable value. Due to the LBP synthesis character, the watermark needs to be integrated with only a few pixels of the given block. The results show that rotational, JPEG compression, and scalable attacks are robust with the technique. This LBP synthesis could also be used to justify ownership using watermark sensor data.

Towards Source-Based Classification of Image Inpainting Techniques: A Survey

Image inpainting is a process of reconstructing an incomplete image from the available information in a visually plausible way. In the proposed framework, existing image inpainting methods are classified in a new perspective. The information which is referred to, while reconstructing an image, is a critical factor of inpainting algorithms. Source of this information can be host image itself or an external source. The proposed framework broadly classifies inpainting algorithms into introspective and extrospective categories based on the source of information. Various parameters influencing the algorithms under these categories are identified in the proposed framework. A comprehensive list of all publicly available datasets along with the references are also summarized. Additionally, an in-depth analysis of the results obtained with the surveyed techniques is performed based on quantitative and qualitative parameters. The proposed framework aids the user in identifying the most suitable algorithm for various inpainting scenarios.

Data hiding in encryption–compression domain

This paper introduces a robust and secure data hiding scheme to transmit grayscale image in encryption-then-compression domain. First, host image is transformed using lifting wavelet transform, Hessenberg decomposition and redundant singular value decomposition. Then, we use appropriate scaling factor to invisibly embed the singular value of watermark data into the lower frequency sub-band of the host image. We also use suitable encryption-then-compression scheme to improve the security of the image. Additionally, de-noising convolutional neural network is performed at extracted mark data to enhance the robustness of the scheme. Experimental results verify the effectiveness of our scheme, including embedding capacity, robustness, invisibility, and security. Further, it is established that our scheme has a better ability to recover concealed mark than conventional ones at low cost.

An Adaptive Visible Watermark Embedding Method based on Region Selection

Aiming at the problem that the robustness, visibility, and transparency of the existing visible watermarking technologies are difficult to achieve a balance, this paper proposes an adaptive embedding method for visible watermarking. Firstly, the salient region of the host image is detected based on superpixel detection. Secondly, the flat region with relatively low complexity is selected as the embedding region in the nonsalient region of the host image. Then, the watermarking strength is adaptively calculated by considering the gray distribution and image texture complexity of the embedding region. Finally, the visible watermark image is adaptively embedded into the host image with slight adjustment by just noticeable difference (JND) coefficient. The experimental results show that our proposed method improves the robustness of visible watermarking technology and greatly reduces the risk of malicious removal of visible watermark image. Meanwhile, a good balance between the visibility and transparency of the visible watermark image is achieved, which has the advantages of high security and ideal visual effect.

Blind image watermarking scheme based on lowest energy contourlet transform coefficient and modified arnold cat/ikeda maps

<span>The widespread of global internet has led to the need for developing new methods of protecting multimedia information from exploitation, alteration or forgery, illegal distribution, and manipulation. An attacker is quickly and illegally distributing or changing multimedia information by using various means of computer technology. For detecting this manipulation, this paper suggests blind watermark image inside a host image for observing in the receiver. If the watermark image was retrieved, then the host image was not attacked or manipulated. While if not retrieved, in this case, the image was attacked. The proposed method is depending on a decomposition of the host image using lowest energy sub-bands of Contourlet transform (4-levels), with scrambling by Ikeda map of the watermark image, and selecting new positions by modified Arnold Cat map. This will produce more security and safety, as well as provide more difficulty or prevent hacking. The obtained results confirm the robustness against attacks and more effectiveness of the presented scheme compared with the other similar works. Also, using lowest energy sub-bands will expand area of embedding and this part will be considered in the future works with the color images.</span>

Convolutional Neural Network-Based Digital Image Watermarking Adaptive to the Resolution of Image and Watermark

Digital watermarking has been widely studied as a method of protecting the intellectual property rights of digital images, which are high value-added contents. Recently, studies implementing these techniques with neural networks have been conducted. This paper also proposes a neural network to perform a robust, invisible blind watermarking for digital images. It is a convolutional neural network (CNN)-based scheme that consists of pre-processing networks for both host image and watermark, a watermark embedding network, an attack simulation for training, and a watermark extraction network to extract watermark whenever necessary. It has three peculiarities for the application aspect: The first is the host image resolution’s adaptability. This is to apply the proposed method to any resolution of the host image and is performed by composing the network without using any resolution-dependent layer or component. The second peculiarity is the adaptability of the watermark information. This is to provide usability of any user-defined watermark data. It is conducted by using random binary data as the watermark and is changed each iteration during training. The last peculiarity is the controllability of the trade-off relationship between watermark invisibility and robustness against attacks, which provides applicability for different applications requiring different invisibility and robustness. For this, a strength scaling factor for watermark information is applied. Besides, it has the following structural or in-training peculiarities. First, the proposed network is as simple as the most profound path consists of only 13 CNN layers, which is through the pre-processing network, embedding network, and extraction network. The second is that it maintains the host’s resolution by increasing the resolution of a watermark in the watermark pre-processing network, which is to increases the invisibility of the watermark. Also, the average pooling is used in the watermark pre-processing network to properly combine the binary value of the watermark data with the host image, and it also increases the invisibility of the watermark. Finally, as the loss function, the extractor uses mean absolute error (MAE), while the embedding network uses mean square error (MSE). Because the extracted watermark information consists of binary values, the MAE between the extracted watermark and the original one is more suitable for balanced training between the embedder and the extractor. The proposed network’s performance is confirmed through training and evaluation that the proposed method has high invisibility for the watermark (WM) and high robustness against various pixel-value change attacks and geometric attacks. Each of the three peculiarities of this scheme is shown to work well with the experimental results. Besides, it is exhibited that the proposed scheme shows good performance compared to the previous methods.

A Novel Dual Image Watermarking Technique Using Homomorphic Transform and DWT

In this paper a new technique of dual image watermarking is proposed for protection of ownership rights which utilizes salient properties of homomorphic transform (HT), discrete wavelet transform (DWT), singular value decomposition (SVD) and Arnold transform (AT). In embedding algorithm host image is splitted into reflectance and illumination components using HT, DWT is further applied to the reflectance component resulting in frequency subbands (HL and LH) which are transformed by SVD. Two image watermarks are selected for embedding process whereas security of proposed algorithm is strengthen by performing scrambling of second watermark through AT. Both watermarks are transformed with DWT and SVD. Singular values (SVs) of both transformed watermark are embedded into SVs of host image. Simulation results clearly signifies for high robustness and imperceptibility of proposed algorithm as it is examined under various attacks. Superiority of proposed technique is illustrated by comparing it with other reported methods.

Robust and Blind Multiple Image Watermarking Using CNN and DWT in Video

Digital watermarking was introduced as a result of rapid advancement of networked multimedia systems. It had been developed to enforce copyright technologies for cover of copyright possession. Due to increase in growth of internet users of networks are increasing rapidly. It has been concluded that to minimize distortions and to increase capacity, techniques in frequency domain must be combined with another technique which has high capacity and strong robustness against different types of attacks. In this paper, a robust multiple watermarking which combine Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT)and Convolution Neural Network techniques on selected middle band of the video frames is used. This methodology is considered to be robust blind watermarking because it successfully fulfills the requirement of imperceptibility and provides high robustness against a number of image-processing attacks such as Mean filtering, Median filtering, Gaussian noise, salt and pepper noise, poison noise and rotation attack. The proposed method embeds watermark by decomposing the host image. Convolution neural network calculates the weight factor for each wavelet coefficient. The watermark bits are added to the selected coefficients without any perceptual degradation for host image. The simulation is performed on MATLAB platform. The result analysis is evaluated on PSNR and MSE which is used to define robustness of the watermark that means that the watermark will not be destroyed after intentional or involuntary attacks and can still be used for certification. The analysis of the results was made with different types of attacks concluded that the proposed technique is approximately 14% efficient as compared to existing work.

Computational Intelligence in the Reversible Data-Hiding Processes Using Radon Transform

Data hiding has emerged as a major research area due to the phenomenal growth in internet and multimedia technologies. Securing data transmitted over the internet becomes a challenging issue caused in digitization and networking over the past decade. Data hiding schemes have been adopted to protect digital media content which involves confidential data such as text, video, audio, images, and compression coding. A good reversible data hiding scheme is characterized by the possession of attributes like reversible, imperceptible, high payload capacity, and robustness. By reversible, it's meant that the extraction of the payload as well as the restoration of the host image perfectly from the stego image. Secondly, the imperceptible stego image resemblance against the cover/host image. Finally, robustness counts for the ability to sustain the secret payload against both intentional and unintentional attacks; it has been observed that all the proposed algorithms are more robust and reversible against various attacks in lower bit error rate and higher normalization coefficient.