1. Introduction
With the fast development of information technology, multimedia data has become the most important carrier for information transmission. Digital images, as one of the most important ways for transmitting the information in one or more images, can be easily altered and destroyed by the developed techniques. Thus, to protect the authenticity and integrity of images, schemes applied for copyright protection of images can be essential and meaningful. Based on this purpose, there are mainly two methods proposed to overcome the above problems, which are digital signature [
1,
2] and digital image watermarking [
3,
4,
5]. Digital signature is a kind of number string produced by the sender, which can be used as the secret key for both senders and receivers. But it can only detect that images have been tampered or not, and it cannot identify the tampered region location. Thus, watermarking technique is proposed as an effective method to solve the copyright issue of image contents. The categories of digital watermarking algorithms can be divided on the basis of their different robustness and functions: robust watermarking, semi-fragile watermarking, and fragile watermarking. Robust watermarking, as its name implies, should have robustness to all kinds of attacks, which is used for copyright protection. On the converse, fragile watermarking is sensitive to image modification, which includes malicious tampering and un-malicious processing. The last one is semi-fragile watermarking, which can be utilized to make the judgement between malicious tampering and non-malicious modification. Actually, semi-fragile watermarking integrates advantages in robust and fragile watermarking with each other. In addition, semi-fragile watermarking is superior to fragile watermarking when considering the ability of resisting common image operations. In perspective of the domain where the watermark works, watermarking technique can be categorized as spatial or frequency domain [
6]. The embedding method of watermark information in spatial domain methods is to directly alter the pixel value of the digital image, and advantages of spatial domain watermarking are easy implementation and low computational complexity. But it has shortcoming that spatial watermarking is not robust to some image processing operations in some degree. Accordingly, the frequency domain methods embed the watermark information by use of modifying frequency coefficients of the original image after transforms. Compared with spatial methods, with the help of mathematical transform, frequency domain watermarking has the better imperceptibility and robustness. There are common mathematical transforms applied in the frequency domain watermarking: discrete wavelet transform (DWT), discrete cosine transform (DCT), singular value decomposition (SVD), and discrete Fourier transform (DFT) [
7]. Many typical watermarking schemes are proposed as well as applied in the field of the medical research. Based on Lagrangian support vector regression (LSVR) and lifting wavelet transform (LWT), Mehta et al. [
8] proposed an efficient image watermarking scheme, where the Arnold scrambled watermark is embedded into the selected blocks from low frequency sub-band by one level DWT. Makbol et al. [
9] presented an innovative image watermarking scheme based on SVD and integer wavelet transform (IWT) to overcome the false positive problem (FPP). In [
9], the singular matrix of the watermark is embedded into the singular values of the host image. To obtain the optimized scaling factor, multi-objective ant colony optimization (MOACO) is utilized. Rasti et al. [
10] proposed a colour image watermarking algorithm to divide the host image into three channels and calculate the entropy of the patches obtained in the blocks. Certain patches are selected according to the comparison with a predefined threshold for further transforms to embed the watermark. In the aspect of functional magnetic resonance imaging (FMRI) protection, Castiglione et al. [
11] introduced a fragile reversible watermarking scheme for achieving authenticity and integrity. In the field of microscopy images protection, Pizzolante et al. [
12] introduced a novel watermarking scheme to embed the watermark information into the confocal microscopy image.
Traditional watermarking algorithms have robustness to the common attacks, such as noise addition, filter operation, cropping, and so on. However, owing to the unique features of geometric attacks, the watermark synchronization can be destroyed, bringing about the failure in watermark extraction [
13]. Thus, it is urgent to propose watermarking algorithms which have resistance to geometric attacks. In recent years, there have been many schemes proposed to solve these problems, which are based on Zernike moments [
14], harmonic transform [
15], feature points [
16], and so on. Due to the fact that SIFT is efficient for the application obtaining the image feature and matching the two images, which makes it more suitable for watermark embedding. However, the scale-invariance feature transform (SIFT) has some advantages: firstly, the significant amount of feature points can be extracted with appropriate parameter settings; secondly, the image feature extracted by SIFT has great uniqueness, which is suitable for accurate matching; finally, SIFT features are invariant to the rotation, scaling, and translations [
17], which can be applied as an efficient tool for robust watermarking to acquire the robustness to the geometric attacks. Lee et al. [
18] introduced to use local invariant feature for embedding the watermark into the patches of circle shapes generated by SIFT, and proposed an innovative image watermarking scheme. To deal with the watermark synchronization errors, Luo et al. [
19] proposed an innovative watermarking scheme based on DFT and SIFT. Based on two techniques, SIFT and DWT, Lyu et al. [
20] presented an image watermarking scheme, performing the DWT on the SIFT areas which are selected for watermark embedding. Thorat and Jadhav [
21] proposed a watermarking scheme resistant to the geometric attacks based on IWT and SIFT, where SIFT is utilized on the red channels, and the feature points are extracted. Then, blue and green components are performed by IWT, and low-frequency coefficients can be extracted for watermark embedding. In [
22], Pham et al. introduced a robust watermarking algorithm on the basis of SIFT and DCT, where the watermark information is embedded into the specific feature region performed by DCT. In [
23], based on SVD and SIFT, Zhang and Tang proposed a robust watermarking scheme for solving the watermark synchronization problem, and SIFT is applied for watermarking resynchronization. To deal with the issue of copyright protection for depth image based rendering (DIBR) 3D images, Nam et al. [
24] proposed a SIFT features-based blind watermarking algorithm, where feature points are extracted from different view images. Besides, a watermark pattern selection algorithm based on feature points orientation and spread spectrum technique for watermark embedding are applied in this algorithm. In [
25], Kwawamura and Uchida presented a SIFT-based watermarking method, which is evaluated by the information hiding criteria (IHC). The local feature regions around SIFT features are applied for scaling and rotation robustness, and two error correction algorithms are used, which are weighted majority voting (WMV) and low density parity check (LDPC) code to correct the errors of extracted watermarks. As the fast algorithm compared with SIFT, speed up robust feature (SURF) algorithm is applied into watermarking algorithm. Fazli and Moeini [
26] presented a geometric-distortion resilient watermarking algorithm, using the fuzzy C-means clustering to process the feature points extracted by SURF, and extracted feature point sets are used to divide the image into triangular patches for watermark embedding.
However, the traditional SIFT algorithm can match feature points under the condition of rotation and scaling. As for the tilted image, the feature extracted from the image can be of a small quantity. Concretely, the SIFT algorithm has the feature of scale invariance instead of affine invariance, which can result in the limitation that extraction for an image whose shooting angle changes with a large angle is difficult. In this paper, a novel robust watermarking based on affine-scale-invariance feature transform (ASIFT) [
27] in the wavelet domain is presented. Firstly, DWT is performed on the host image for three times, and SVD is operated on the selected low frequency component in horizontal and vertical directions (LL) sub-band. Secondly, the watermark information is embedded into the obtained three-level LL sub-band. The ASIFT points are saved as feature keys for the correction of attacks. Thus, anti-attacks capability can be attained with the help of matching ASIFT feature points in the extraction phase. Finally, experimental results demonstrate that the proposed scheme is imperceptible and resilient to common image processing such as Gaussian noise, salt and pepper noise, speckle noise, median filters, cropping, and so on. Especially for geometric attacks, it has better performance than SIFT-based watermarking.
The remainder of this paper is arranged as follows: in
Section 2, the related work is reviewed, including the theories of DWT, SVD, and ASIFT;
Section 3 introduces the distortion correction by the ASIFT points;
Section 4 gives concrete watermark embedding and extraction procedure; comparison with previous schemes in evaluation of robustness and imperceptibility and demonstration of the advantages in the proposed scheme are given in
Section 5; conclusions and future work are illustrated finally in
Section 6.