1. Introduction
Advancements in multimedia technologies and computer networks have made duplication and distribution of digital contents such as audio, video or digital images, much easier than ever before in recent years. Protection of such digital content has become a challenging task from a security and copyright point of view. To address these issues, the digital watermarking scheme was introduced [
1,
2]. The main concern of the digital image watermarking scheme is to maintain the quality of the host image showing good robustness against distortion attacks. Watermarking schemes can be categorized according to various factors, such as the domain in which the watermark is inserted, the spatial or frequency domain, visible or invisible watermarking, robust or fragile watermarking. A very brief discussion on these schemes is provided in the paragraphs below. However, for more details on watermarking schemes, the interested researchers may refer to [
3,
4].
The spatial domain watermarking schemes directly insert the watermark into the host image by modifying the pixels intensities [
5,
6,
7]. Modifying the least significant bits (LSB) of the host image pixels to insert watermark bits is the simplest scheme in this category [
5]. The spatial domain watermarking schemes are easy to implement, having a low cost of operation, but generally are not robust against image distortion attacks.
Frequency domain methods first transform the spatial representation into the frequency domain and then modify the frequency coefficients. Literature also reveals the implementation of various transforms, such as the discrete wavelet transform (DWT), discrete Fourier transform (DFT), quaternion wavelet transform (QWT), discrete fractional Fourier transform (DFrFT), singular value decomposition (SVD), discrete cosine transform (DCT), and their combinations, for image watermarking schemes [
8,
9,
10,
11,
12,
13,
14,
15,
16]. A redistributed invariant discrete wavelet transform (RIDWT) image watermarking technique was introduced by Li et al. [
17]. This transform is invariant to the ninety-degree multiple rotations, row, and column flipping and is obtained by shifting the pixels of the image to the new locations and then applying wavelet transform and some normalization process.
It can be identified from the literature review of DCT-based image watermarking schemes that, generally, the watermark is inserted into the host image in the frequency domain by modifying the frequency coefficients [
10,
11]. Implementation of DCT is a time taking process. Keeping this fact in mind, some researchers implemented watermark insertion into DC values computed in the spatial domain without using the DCT [
18,
19,
20,
21]. The host image is divided into 8 × 8 sub-blocks and DC coefficients are computed in the spatial domain for each block instead of applying the discrete cosine transform (DCT). Watermark bits are inserted by modifying DC coefficients of each block in the spatial domain. The robustness of these schemes is more or less the same as in transform domain schemes, because these schemes mimic the behavior of a transform domain scheme in the spatial domain.
Utilization of machine learning and optimization techniques, such as support vector machines, neural networks, fuzzy logic, and evolutionary algorithms (EAs) have played an important role in image watermarking [
22,
23,
24].
Genetic algorithm (GA), artificial bee colony (ABC), particle swarm optimization (PSO), firefly algorithm (FA), differential evolution (DE), and teaching–learning-based optimization (TLBO) are a few examples from a long list of evolutionary algorithms (EAs) that have made several valuable contributions to watermarking. Some references are: determination of optimal scaling factors for watermark insertion using GA [
22,
24,
25]; utilization of PSO in real life problems, including digital watermarking [
23,
26,
27,
28]; implementation of DE for finding the optimal parameters [
29,
30]. More recent applications include use of artificial bee colony (ABC) and FA for determining the optimal parameters [
31,
32,
33,
34].
In the present study, the focus is on differential evolution, an easy to implement, simple, and robust evolutionary algorithm. DE has gained popularity in being a good optimizer for solving diverse real-life application problems [
35]. It is worth mentioning here that the DE has already been utilized successfully in image watermarking [
29,
30], but to the best of our knowledge, it has never been practiced on DC-based image watermarking in the spatial domain.
All of the above-mentioned DC-based watermarking schemes, without using DCT, are not robust to the ninety-degree multiple rotations and flipping attacks. These are the simple attacks that change the pixel location without changing the intensity to destroy the inserted watermark.
Furthermore, the quantization parameter used for watermark insertion and extraction is tuned and adjusted manually. A constant quantization parameter is not a suitable choice, as a different kind of image may have a different tolerance limit of modification. These problems can be solved by finding a mechanism of getting invariant features and an optimal quantization parameter for watermark insertion and extraction. The proposed watermarking scheme is motivated by the invariant property of the DC value of a dataset. The order of the entries in a dataset does not matter, the different permutation of the dataset will have the same DC values. Using this concept, the pixels in the host image are redistributed to a different location in such a way that the image blocks must have the same pixel values under the ninety-degrees multiple rotations, row and column flipping. If the blocks have the same pixel values under these operations, then obviously the DC values will be invariant. The host image is divided into blocks, DC values are calculated in the spatial domain without using DCT and then modified using the optimal quantization parameter obtained through the differential evolution (DE) algorithm to get the new DC values. The difference is calculated between the old and new DC values, and then the pixel values in the block are changed in such a way that the total amount of change is equal to the difference in the DC values. The performance of the proposed watermarking scheme is investigated by taking seven standard test images and two watermarks, and then employing various common image manipulation attacks. Results are compared with the other similar watermarking schemes available in the literature utilizing some well-known evaluation metrics, peak signal to noise ratio (PSNR), structural similarity index measure (SSIM), and normalized correlation (NC). Results analysis validates that the proposed watermarking scheme is robust against image distortion while maintaining the good quality of the watermarked image.
The structure of the paper is: invariant DC coefficient computation and the modification are explained in the
Section 2.
Section 3 describes the proposed watermarking scheme, and results analysis and comparisons are provided in
Section 4. Concluding remarks and future research directions are given in the last section.
4. Performance Evaluation and Experimental Discussion
This section is fully dedicated to the performance analysis of the proposed watermarking scheme and its comparison with the other similar type of the watermarking algorithms proposed by Parah et al. [
19] and Zeng et al. [
21]. Both of these schemes have used DC coefficients and constant quantization factor for watermark embedding, but different quantization methods. The proposed scheme uses invariant DC coefficients and the image dependent optimal quantization parameter obtained by the DE algorithm. Seven grayscale standard test images of size 512 × 512, and two binary logos (
W1 and
W2) of size 64 × 64, given in
Figure 4, are considered for the performance evaluation of the proposed scheme. These test images are collected from various open image databases that are freely available. For the investigation of the robustness of the proposed scheme, various common image distortion attacks, given in the
Table 1, are applied to make a dent in the quality of the watermarked image (
Iw). The algorithm is coded in MATLAB and executed on a personal computer (PC) with Intel core
i5 processor, Windows 8 and 4 GB RAM. Experimental results are given in
Table 1,
Table 2,
Table 3,
Table 4,
Table 5,
Table 6 and
Table 7. In
Table 6, the best results are highlighted in bold and the tie cases are highlighted in italics.
4.1. Imperceptibility Analysis
The watermark inserted into the host or cover image must be imperceptible in case of invisible watermarking. This property is related to the human visual system. Generally, a watermarking scheme is said to be imperceptible if both the images, original and the watermarked, are mutually identical. A good watermarking scheme does not degrade the quality of the host image in the watermark insertion process. To analyze the imperceptibility of the watermarked images, several evaluation metrics are available in the literature [
29,
30,
31,
32,
33,
34,
35,
36,
37,
38,
39]. Peak signal to noise ratio (
PSNR) and structural similarity index measure (
SSIM) [
18] are the two most frequently used imperceptibility evaluation metrics. Following the same trend, this study uses these two metrics to analyze the imperceptibility. In
Table 2 and
Table 3,
PSNR and
SSIM values are listed, obtained by the watermarking schemes for the comparison of imperceptibility. The proposed watermarking scheme is targeted to achieve forty-five
PSNR value. It can be seen from
Table 2 that the
PSNR values for all the images are very close to 45 obtained by the proposed scheme, whereas the other algorithms are stuck near about 42 and 44. With a close observation of average
PSNR, the proposed algorithm is better than the other algorithms. The average
PSNR obtained by the proposed scheme is almost 5% higher than the other schemes. Similar types of responses can be seen from
Table 3, which provides the
SSIM. The proposed watermarking scheme provides better results in all the cases in comparison to the other algorithms.
4.2. Robustness Analysis against Attacks
This section is dedicated to the robustness analysis employing the image distortion attacks to the watermarked image given in
Table 1. Normalized correlation (
NC) given in Equation (22) is used to evaluate the quality of the extracted watermark. The extracted watermark is more similar to the inserted watermark as the
NC value approaches closer to one. Results are given in
Table 4 and
Table 5 for the extracted watermark1 (
W1) and watermark2 (
W2), respectively. From
Table 4 and
Table 5, it can be seen that the proposed watermarking scheme survives against all the distortion attacks, having good
NC values. Normalized correlation value 1 in cases of ninety-degree rotation, row and column flipping is the evidence that the proposed watermarking scheme extracted the watermarks the same as inserted. It is the main aim of the study that has achieved 100%. Average normalized correlation values over all the seven test images corresponding to each distortion attack are calculated and are given in
Table 6 for comparison of the watermarking schemes. From
Table 6, looking at the results, it can be said that all the schemes performed equally, having the average
NC values 1 in cases without distortion and pixelation distortion attacks. In the remaining cases out of the thirteen, the proposed scheme performed better in seven cases (more than 50%) whereas the other schemes performed well in six cases. Having a look at the overall average
NC values, we can say that the proposed scheme is able to achieve almost 7% higher values in comparison to the other schemes. Sample images of the attacks applied to the watermarked image “Lena” and the extracted watermarks are given in
Figure 5 for the visual quality comparison of the schemes. Due to the space constraints, there is only one watermark shown in
Figure 5. It is evident from the
Figure 5, that the extracted watermark by the proposed scheme can be identified by naked eyes without any difficulty in all the cases, whereas the extracted watermarks by the other schemes are not visually clear in cases of ninety-degree rotation, row and column flipping. Therefore, the proposed scheme outperformed the other watermarking scheme in the competition. In some cases, mean filtering, Gaussian noise, JPEG compression, rescaling, median filtering, motion blur, the proposed scheme performed opposite to expectation in comparison to other schemes. Further studies need to investigate the reasons for not performing according to the expectation in these particular cases.
4.3. Execution Time Analysis
This section presents the computational complexity analysis of the proposed scheme in terms of time required for watermark insertion and extraction, and the results are given in
Table 7. The main objective of copyright protection watermarking applications is to establish ownership of the owner, irrespective of time, and it is not a crucial factor in such kind of applications. While in broadcast monitoring applications, insertion and detection are performed in real time and this important factor cannot be neglected. It can be seen from
Table 7 that the watermark insertion time of the other schemes is very low, whereas the extraction time is more or less same. This is because, not only the invariant DC values obtained in the proposed scheme, but also the differential evolution algorithm is used to get the optimal quantization parameter for the insertion process. Briefly, in the other watermarking schemes, watermark bits are inserted by modifying DC coefficients using a predefined quantization parameter. Hence, the proposed scheme is computationally complex in comparison to the other schemes.
4.4. Security and False Positive Detection of Watermark
Several researchers in the watermarking literature have observed false-positive detection problems in various digital image watermarking schemes. It happens generally due to the insertion of partial information of the watermark into the host image instead of complete watermark and partial watermark information being kept safe with the owner that is provided back at the time of the watermark extraction. The ownership problem or dispute remains unsolved in such a condition, as it creates an ambiguous situation. Insertion and extraction of the entire watermark instead of partial information are one of the solutions to this problem. A person who claims ownership of the image needs to extract the entire watermark from the watermarked image to prove it. Following the same principle, the proposed scheme inserts the entire watermark and extracts the entire watermark that makes it free from false-positive detection problems.
Furthermore, the inserted watermark has one extra layer of security that is provided by piecewise linear chaotic map (PWLCM) using two secret keys, control parameter and starting point. Without having these two correct keys it is impossible to extract the inserted watermark. Therefore, false-positive detection problems do not occur in the proposed scheme.
5. Conclusions
This study proposed a robust watermarking scheme based on DC values invariant to ninety-degrees multiple rotation, row and column flipping, by modifying the pixel values in the spatial domain. Modification for watermark insertion into the host is based on a quantization parameter that is optimally obtained by DE algorithm. This study provides another scope for improving the image watermarking scheme by using invariant DC and optimal parameters. The strength of the proposed watermarking scheme is investigated by taking seven standard test images and applying various image distortion attacks. The proposed watermarking scheme survived against most of the attacks considered in this study very well, which can be seen numerically as well as through images. It is seen that the performance of the proposed scheme on average is better than the other schemes considered in the comparison, in terms of imperceptibility and robustness. However, in some cases, mean filtering, Gaussian noise, JPEG compression, rescaling, median filtering, motion blur, the performance of the proposed scheme is not at par with other schemes. Further studies need to investigate the reasons for the dull performance of the proposed scheme in these particular cases. Excited by the performance of the proposed scheme, its extension for the video, audio and colored image watermarking is one of the future research plans.