Digital watermarking with improved SMS applied for QR code

https://doi.org/10.1016/j.engappai.2020.104049Get rights and content

Abstract

With the rapid development of information technology, infringements have become increasingly serious. Digital watermarking is an effective method to protect information. The current watermarking technology still has room for further improvement in imperceptibility and robustness. This paper proposes an improved watermarking technology using meta-heuristic algorithm. Further, Quick Response code (QR code) is used as a carrier to transmit information. The improved Discrete Wavelet Transform-Singular Value Decomposition (DWT-SVD) is used to hide the watermark into the QR code. Therefore, digital watermarking is realized on the QR code. In the common watermark embedding methods, the digital watermark is related to the embedding strength. How to find a suitable embedding factor and reduce distortion is of great significance to these watermarking algorithms. This paper mainly proposes two novel algorithms based on States of Matter Search (SMS) algorithm to find suitable embedding factors. The first algorithm uses an adaptive parameter to control the movement of particles called the adaptive step States of Matter Search (sSMS). The second algorithm incorporates co-evolutionary matrix to enhance the search capability named Co-evolution States of Matter Search (CSMS). DWT-SVD is updated through two algorithms to acquire optimal embedding strength factors on the QR code watermarking. By adjusting the embedding strength factors, the intensity of the watermark embedded in different frequency domains would be modified. The experimental results have higher PSNR and the QR code can still be decoded by a general decoder. It shows that the proposed approaches are practicable and effective.

Introduction

Digital watermarking technology directly embeds some secret information into multimedia content through a certain algorithm. It will not affect the value and use of the original content, and will not be noticed by human intuition. With the development of theoretical research, technology has been developed sharply in practical application, such as copyright protection and information hiding. Digital watermarking technology should be safe, that is, it cannot be tampered with. It should be not easily detected, and will not affect the normal use of the original carrier. It should be robust, which means that after suffering one or more attacks, the watermark can still be extracted completely. The sensitivity means that digital watermarking can determine whether the data has been tampered. Further, the location and the degree of damage can be judged.

The most common watermarking techniques include the Least Significant Bit (LSB), Discrete Cosine Transform (DCT), DWT, SVD, and Discrete Fourier Transform (DFT). Tirkel et al. propose two methods to add a watermark to the LSB of a gray-scale image (Van Schyndel et al., 1994). Subsequently, O’Ruanaidh et al. propose DCT, which is a watermarking scheme based on spread spectrum communication in 1996 (O’Ruanaidh et al., 1996). Lu et al. propose a robust image-based on DCT in 2010 (Lu et al., 2010). DWT and DFT are the most commonly used watermarking techniques (Ganic and Eskicioglu, 2004). Kundur et al. come up with embedding the watermark into the wavelet domain (Kundur and Hatzinakos, 1998). The wavelet transform deals with the image signal and exploits a series of wavelets of different scales to decompose the original function. After the transformation, the coefficients of the original function under different scales of wavelet are obtained. The wavelet transforms algorithm has the advantage of keeping the original anti-filtering and compression attacks.

At present, the Discrete Wavelet Transform (DWT) has been widely exploited in the digital image, video, audio, and other fields. So far, there are various improved DWT algorithms that have effectively improved the robustness and image quality of embedded watermark. But they are still not insufficient to resist multiple attacks. SVD is a special matrix transformation. It is also widely used in watermark processing (Liu and Tan, 2002, Mohammad et al., 2008). Mohammad et al. present a new semi-blind reference watermarking scheme in 2009 (Bhatnagar and Raman, 2009). A new combination method DWT-SVD appears, which combined DWT with SVD.

Computational intelligence algorithms are mostly stemmed from biological behaviors in nature, and most of them have strong optimization ability, which is a powerful tool to solve problems in actual scenarios (Corchado et al., 2010, Pan et al., 2020, Kim et al., 2007). Computational intelligence includes three basic areas: fuzzy computing, neural networks, and evolutionary computing. In the last few years, a good deal of naturally inspired computation methods is proposed. They have their expansibilities and have been widely used in engineering and daily life. Classical examples are genetic algorithm (GA) by simulating the genetic evolution in the world (Abraham et al., 2006), particle swarm optimization (PSO) by simulating bird swarm movement (Kennedy and Eberhart, 1995). There are many intelligent optimization algorithms proposed in nature, such as multiverse optimizer (MVO) (Mirjalili et al., 2016), pigeon-inspired algorithm (PIO) (Duan and Qiao, 2014, Tian et al., 2020), differential evolution algorithm (DE) (Hu et al., 2014, Das et al., 2008, Meng et al., 2019), cat swarm algorithm (CSO) (Chu et al., 2006, Tsai et al., 2008), flower pollination algorithm (Nabil, 2016, Nguyen et al., 2019), QUasi-Affine TRansformation Evolution (QUATRE) (Pan et al., 2016, Du et al., 2020, Sun et al., 2020), gray wolf algorithm (GWO) (Emary et al., 2015, Hu et al., 2019) and so on.

Generally speaking, the objective is to get a globally optimal solution under the given conditions in many algorithms. SMS is proposed by Erik Cuevas et al. which is inspired by the physical principles of thermal-energy motion mechanism in 2014 (Cuevas et al., 2014). Compared with other evolutionary algorithms, it improves balance and has better searchability. The relationship between exploration and exploitation is adjusted in our new approach. In this paper, we mainly study more scientific methods based on adaptive step SMS (sSMS) and significative Co-evolution States of Matter Search (CSMS). CSMS has a wonderful property with fewer hardware demands. It effectively improves the balance between exploration and exploitation.

The embedding factors in the watermarking technique affect the imperceptibility and robustness of the image. They are studied to find the optimal values for QR code watermarking. Ying Yang et al. determine the adaptive embedding factor by establishing a mathematical model on Human Visual System (HVS) characteristics (Yang et al., 2008). C. Patvardhan et al. hide watermark by converting from RGB color space to YCbCr space to exploit characteristics of the HVS in 2018 (Patvardhan et al., 2018). Later, some computational intelligence algorithms are widely applied on watermarking methods, including PSO (Saxena et al., 2018), GA (ElShafie et al., 2008) and quantum-inspired evolutionary algorithm (Samanta et al., 2017).

This paper mainly uses our novel algorithms to obtain a robust QR code watermarking. Before the watermark is embedded, the scrambling algorithm combining Arnold transform and Logistic transform is performed on the watermark. This method not only changes the original appearance of the watermark but also alters its information. To some extent, the security of watermarking is improved, which is conducive to ensure the secondary encryption of this algorithm. The QR code is a practical tool and has a security mechanism (Liu et al., 2019). Lastly, we combine the improved methods with the QR code. It cannot only be decoded correctly but also the watermark can be extracted faultlessly. In addition, the watermarked QR code can resist some attacks. Therefore, this watermarked QR code carries two-level information. It is difficult to find the watermark.

The purposed approach applies information encryption and hides information into a QR code. Our main contribution is to design two novel algorithms to obtain lower distortion and stronger robustness for the QR code watermarking technique. In addition, they have the ability to resist some attacks.

The rest of this paper is organized as follows. Basic concepts of original SMS, encryption methods, DWT, and SVD are introduced in Section 2. Section 3 proposes two novel algorithms based on SMS, including sSMS and CSMS. The optimization process for DWT-SVD and the adaptive embedding strength factors are illustrated in Section 4. The combination with the QR code watermarking is shown as well. Section 5 is the experimental results. Finally, the conclusion is given in Section 6.

Section snippets

Related work

In general, the watermark model is organized by two parts, one part is the watermark embedding and the other is the extraction of this watermark. The section provides a theoretical basis for the research. The basis for optimization is also derived.

The designs for improved SMS

In this section, the two schemes of optimization based on SMS are described. For the goal of this design, we take minimization under the restrict conditions as the optimal value in our algorithms.

The proposed optimization-based embedding and extraction for digital watermarking with improved SMS

In this section, the proposed optimization-based embedding with the optimal embedding factors on DWT-SVD and extraction is described. This way is performed on the QR code.

Experiment and performance analysis

This section mainly discusses the evaluation of the proposedoptimization-based embedding factors for digital watermarking. It is mainly divided into three parts: benchmark test functions, simulation of novel DWT-SVD technique, the application in the QR code.

Conclusions and future prospect

The study puts up with two novel algorithms based on basic SMS. CSMS and sSMS are combined with DWT-SVD in digital watermarking for low distortion and robustness like PSO (Saxena et al., 2018), CS (Dey et al., 2013), and ABC (Sharma et al., 2019). To enhance the robustness, the watermark is embedded into the low-frequency DWT coefficients. These coefficients are decomposed with SVD. Based on the better embedding scaling factors using our algorithms, the watermark information is embedded into

CRediT authorship contribution statement

Jeng-Shyang Pan: Conceptualization, Methodology, Supervision. Xiao-Xue Sun: Methodology, Software, Writing - original draft. Shu-Chuan Chu: Data curation, Methodology. Ajith Abraham: Writing - review & editing. Bin Yan: Software, Writing - original draft.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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