1 Introduction

With the advancement of internet technologies, an increasing amount of media data is easily distributed, stored and shared on different social media and other plateforms such as Linkedin, Facebook, Twitter and Flickr [1]. Using digital images is a most common way to share such data over these popular platforms. Sharing of such images might bring issues of privacy leakage, copyright protection, identity theft and the data tampered by any intruder [2, 3]. Therefore, protection of such images has attracted considerable attention for different research community. To overcome such issues, data hiding scheme is widely adopted in the past few years to protect the media content by invisibly concealing secret mark (s) into host media for copy-protection purpose [4]. Watermarking and steganography are the main branch of data hiding scheme. Among the schemes, digital watermarking is widely and actively used method [5]. The research of watermarking scheme is mainly to improve performances in context to the invisibility, embedding capacity and robustness, which is difficult to balance the tradeoff among these performances [2]. According to the operations domains, the watermarking approaches include two significant domains, i.e., spatial and transform [6]. Compared with the spatial-domain scheme, the transform domain-based watermarking schemes are more robust in nature [7].

Considering the advantages and limitations of a single type of domain-based method, this paper introduces a hybrid of spatial and transform-based dual watermarking algorithm. The contributions of this work are as follows:

  • Invisible and robust hiding scheme with high hiding efficiency: The proposed scheme uses fusion of NSCT-HD-SVD [8, 9], which is improving the robustness. The proposed scheme is further improving the embedding capacity by means of pseudo magic cubes [10], so as to avoid the increase in distortion caused by large-capacity embedding.

  • Enhanced security via dual marking and encryption: Dual marks are concealed into the cover media to ensure the copy-protection and content security at the same time. Further, fusion of DNA computing, chaotic and hash function-based encryption scheme [11] provides the additional security of our scheme.

  • Enhanced robustness: The simulation results of the proposed algorithm indicate good robustness effect compared to existing schemes with high security and hiding efficiency.

The remaining chapter is ordered as follows: Sect. 2 gives the relevant works. Section 3 gives the detailed design of the proposed scheme, followed by result analysis in Sect. 4. The work summary along with future directions is presented in Sect. 5.

2 Literature survey

Some related works are briefly described in this section.

Kazemi et.al developed a watermarking algorithm utilized neural network model in the NSCT-domain [12]. Firstly, cover image is transformed using NSCT, and appropriate coefficients are chosen by means of Kurtosis. The method uses genetic algorithm to get the optimum embedding strength, and perceptron neural network is performed at extraction procedure to obtain recovered logo image. Although the method is robust, it required more time to perform training data sets. Thakur et al. [13] designed an improved algorithm, which can invisibly embed dual marks in NSCT-RDWT-SVD domain. Further, chaotic encryption is applied on marked image to ensure confidentiality of the media data.

Ali et.al [14] introduced a PSO-based scheme in NSCT- SWT-SVD domain. In this approach, NSCT decomposed host image into lower frequency and directional coefficient. However, SWT and SVD performed on NSCT coefficient of host image. The embedding factor of this scheme is optimized through PSO scheme optimized by neural networks. In the blind watermarking scheme proposed by Li and Zhang [15], scrambled mark is embedded into carrier image using NSCT and Schur. Although method is invisible and robust, it doesn’t provide better performance against few attacks such as scaling and low pass filtering. Su et.al [16] designed a blind watermarking using NSCT and HD and included a hash algorithm to randomly select the block for embedding binary mark purpose. A watermarking algorithm is introduced for offering the authenticity of digital data in [17]. In embedding part, multiple marks are embedded into cover image through NSCT, RDWT and SVD. After that, marked image is encrypted and compressed to increase the security and reduce bandwidth utilization, respectively. In Ref [18], author discussed a watermarking algorithm using transformed schemes and included wavelet-based compression to reduce the bandwidth utilization. This method increased the security by using dual watermarking and scrambled the signature mark. A robust watermarking scheme has been developed for 3D images in ref [19]. In preprocessing part, cover image is transformed into YUV channel, and sub-block division is performed on Y-component. After that, 3rd level of NSCT is utilized on designated sub-block of cover image. The scrambled mark is placed in the appropriate NSCT cover. Vaidya et.al [20], have introduced a multi-decomposition-based watermarking approach is proposed for copy-protection of color media. Initially, DWT adopted on ‘Y’ part of host media. After that ‘LL’ band is transformed using CT, Schur and SVD. Thereafter, encrypted mark image is concealed in the transformed coefficient of host. To provide the solution of false positive problem, authors have introduced a multi-scale watermarking scheme through IWT and SVD [21]. Objective Evaluation Function (OEF) is applied to provide solution of false positive problem. An optimal factor is obtained by using optimal mapping scheme to balance the tradeoff among watermarking performances. The various NSCT-based watermarking schemes and their limitations are depicted in Table 1.

Table 1 Various NSCT-based watermarking schemes and their limitations
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3 Proposed method

This section provides the detailed description of the proposed algorithm (See Fig. 1). It is divided into different procedures: (a) the mark embedding, (b) marked encryption and (c) recovery. Initially, sub-sampling process is employed to obtain sub-component of cover image. Further, the multiple decomposition (NSCT-HD-SVD) is performed on maximum entropy component of cover image. Similarly, mark image is also transformed. After that, mark image is concealed into singular vector of cover image with the help of appropriate embedding factor to obtain marked image. However, text mark and hash value of cover image are also embedded into the marked image using pseudo magic cubes to obtain final marked image. The fusion of DNA computing, chaotic and hash function-based encryption scheme on marked data provides the additional security of our scheme. Finally, the inverse steps of embedding process are performed to obtain extracted mark image. The process of lossless encryption procedure of the marked data can be seen in Fig. 2. The detail description of embedding procedure, encryption scheme and recovery procedure are presented below in Algorithm 1–3, respectively. Notation and its description used in Algorithm 1 to 3 are depicted below in Table 2.

Fig. 1
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The proposed algorithm

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Fig. 2
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Flowchart diagram of encryption scheme [11]

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figure a
figure b
figure c
Table 2 Notations and its descriptions
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4 Experimental results

The experimental analysis of our implemented algorithm has been simulated on MATLAB 2019a. In the experiment, sixteen gray-images (See Fig. 3) of dimensions 512 \({\text{~}} \times 512\) \(\times 8\) are used for testing [22]. The size of mark image is chosen as 256 \({\text{~}} \times 256 \times 8\) for embedding purpose. The text document of the size 564 characters (500 characters of text information and hash value of cover image of 64 characters) is also chosen for embedding purpose into marked image. In our experiments, PSNR and SSIM [23] between original cover and marked image are used to measure invisibility, NC [23] between original mark and recovered mark are adopted to determine robustness. Further, NPCR and UACI [23] are used to examine the performance of lossless encryption scheme. The performance analysis of our scheme is evaluated at a different gain are depicted in Table 3. It is noticed that the best PSNR and SSIM scores are obtained as 85.41 dB and 1.000, respectively, at gain = 0.015. The best NC score = 0.9996 at gain = 0.1. In order to improve the mark capacity, magic cube-based algorithm is used to embed text data up to 80,000 characters. From Fig. 4, it indicates that the distortion increases at high payload. The NPCR and UACI scores as obtained for encryption scheme are depicted in Fig. 5. In this figure, it is observed that best NPCR and UACI score is found as 0.9964 and 0.4005, respectively.

Fig. 3
figure 3

Used images as cover a Airplane, b Boat, c Barbara, d Brain, e Baboon, f Lena, g Man, h Couple, iSailboat, j Elaine, k Goldhill, l Peppers, m Zelda, n Toys, o House, and mark p Cameraman

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Fig. 4
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Effect on distortion with different payload

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Fig. 5
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NPCR and UACI values of our proposed scheme for different digital image

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Table 3 The invisibility and robustness at nine different gain values
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Table 4 provides the comparative investigation in terms of invisibility of the proposed model with the existing techniques [15, 16, 18, 19]. As shown in Table 4, PSNR and SSIM score of our algorithm is superior than existing related techniques. The embedding capacity of our method is (256 \({\text{~}} \times 256{\text{*}}8\))/ (512 \({\text{~}} \times 512 \times 8\)) = 0.25. We also embed text document of the size 564 characters in marked image to improve mark capacity of our algorithm. Table 5 provides the comparative investigation in terms of capacity of the proposed model with the existing techniques [13, 16,17,18, 23]. As the result indicated, the capacity of our algorithm is superior than existing related techniques [16, 17] except [13] and [18]. However, the capacity of our algorithm is similar to related techniques [23].

Table 4 The comparative investigation in terms of invisibility
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Table 5 Embedding capacity comparison between proposed method and mentioned techniques
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The histogram of plain, encrypted and decrypted image of our proposed scheme is illustrated in Fig. 6. According to Fig. 6, we can notice that encrypted image is similar to noise like image, and it could not be extract original information about plain image. Histogram of encrypted image indicates that it shows in uniform manner which makes very difficult to extract information.

Fig. 6
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Histogram analysis of plain, encrypted and decrypted image of our proposed scheme

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Figure 7 shows the invisibility and robustness performance of our proposed algorithm for fifteen images. It can be seen that when gain = 0.05, the best PSNR, SSIM and NC scores are obtained as 67.36 dB, 1.000 and 0.9996, respectively. The performance our proposed scheme is evaluated in terms of robustness under various image processing attacks are shown in Fig. 8. According to Fig. 8, robustness is evaluated against JPEG attacks with different quality factor. If quality factor of JPEG is improved, then NC score of proposed scheme is also increased. The NC value is obtained more than 0.9875 against average filtering. In median filtering, NC value is obtained more than 0.9948. In Fig. 8, NC score is evaluated as 0.9832 against Histogram Equalization. In salt and pepper noise, NC score is obtained more than 0.9362. In Speckle and Gaussian noise, NCs value obtained more than 0.9360 and 0.9368, respectively. In rotation attack, NC value obtained as 0.9962. The NC score obtained as 0.9969 against scaling attack. In sharpening attack, NC value is evaluated as 0.9968. According to this table, we have observed that NC value of our scheme is more than 0.9360 against mention attacks.

Fig. 7
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Objective measure of our proposed scheme

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Fig. 8
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Obtained watermarked and extracted mark image under various attacks

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Table 6 provides the comparative robustness investigation of the proposed model with the existing techniques [23,24,25] under well-known attacks. From this table, we have noticed that robustness of the model is higher than that of other traditional algorithms except salt & pepper and speckle noise. It indicates a considerable improvement in robustness of up to 96.36% against histogram equalization attack over other schemes.

Table 6 The comparative analysis of NC values with other three techniques
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5 Conclusions

This paper has described an interesting information hiding algorithm that utilizes NSCT-HD-SVD for concealing of mark data. Further, the algorithm uses magic cube algorithm for higher payload and lossless encryption for additional authentication. The results show that compared with the traditional schemes, robustness of our algorithm is significantly improved. The proposed algorithm not only provides the additional level of information security, but also offers the good hiding efficiency. Further, performance of this scheme can be further improved by selecting optimal embedding factor using metaheuristic approaches.