Objective

2

Watermarking algorithm experiments are usually used as a standard to determine intensity parameters. However

the experimental workload is considerable and stochastic. A watermark is embedded in the original image to coordinate invisibility and robustness. The watermarked image has good invisibility but reduces the watermark robustness. A good watermark hiding technology must consider both the invisibility of the watermarked image and the robustness of the watermark and can resist various common attacks at the same time. An adaptive watermarking algorithm based on image block is proposed to balance invisibility and robustness.

Method

2

Scale-invariant feature transform (SIFT) is utilized to extract the original image with a robustness feature as good that of the watermark-embedded area. Before the embedding operation

feature points are filtered to eliminate the points with low contrast and edge point

and the resulting feature points have high stability. Two watermarking experiments with different watermark sizes are conducted. The extraction of the number of feature points is positively correlated with watermark size. When the watermark is extracted

the points are used again to locate the watermark. The feature points of the original carrier image are extracted to form a matrix with the same size as that of the watermarked image. The extracted embedded region is divided into four equal and non-overlapping image blocks. Each image block is decomposed by singular value decomposition (SVD) to obtain two orthogonal matrices

U

and

V

and a diagonal matrix

S

. Matrix

S

is superimposed by the subband of the first-class discrete wavelet transform of the watermark

which is embedded in the encrypted watermark. The watermark matrix is reorganized

and then the feature points are restored to the original image. The size of the embedded strength affects the performance of the extracted watermark. If the embedding intensity is defined as a constant

then it cannot be applied to each experiment type. Random values are also affected by the embedding effect of the watermark. The fitness function of the fruit

$${\rm fly} $$

optimization algorithm (FOA) is used in adaptive embedding of a watermark image. The senses of smell and sight of fruit flies are superior to those of other species. When flies recognize the smell of food

they

$${\rm fly} $$

in the direction of a target or partner. The FOA simulates this behavior. Two sets of watermarks with different sizes are embedded in this study. Two groups of objective function are selected through several experiments to determine the watermark embedding strength and to achieve a good embedding effect. The FOA is used to balance the invisibility and robustness of the watermark. The watermark detection can directly affect images after attacks without correction.

Result

2

Multi-standard experiments are conducted on standard gray images. Standard gray images of Lena

Baboon

and Plane are selected as the original images

and two binary images of "word" and "Liaoning Technical University" are used as watermark images. Peak signal-to-noise ratio (

$${\rm PSNR}$$

) is used to measure the imperceptibility of the watermark. A high

$${\rm PSNR}$$

indicates high imperceptibility. Normalized correlation coefficient (

$${\rm NC}$$

) is adopted to evaluate the similarity between the original and extracted watermarks. A large

$${\rm NC}$$

value means high robustness of the watermark. For a 16×16 watermark image

the optimal embedding intensities of the three gray images

Lena

Baboon

and Plane

are 0.302 7

0.349 6

and 0.377 3

respectively. The

$${\rm PSNR}$$

of the watermarked image is above 45 dB. For a 32×32 watermark image

the optimal embedding intensities of the three gray images

Lena

Baboon

and Plane

are 0.240 1

0.251 1

and 0.217 6

respectively. The

$${\rm PSNR}$$

of the watermarked image is above 43 dB. These values show that the algorithm achieves good invisibility. When the algorithm is not attacked

the extracted watermark images have

$${\rm NC}$$

values above 0.99

and the watermark robustness is high. External attacks should be resisted. The three gray images with watermark are simulated by applying noise

compression

shear

and rotation. The 16×16 watermark image under different attacks presents watermark

$${\rm NC}$$

values above 0.94. The attacks exert minimal influences on the quality of watermark extraction. The rotation

translation

and other geometric attacks on the 16×16 watermark image lead to

$${\rm NC}$$

values below 1. The watermark image loses some pixels in the rotation and translation processes. Therefore

in the process of watermark extraction

all pixels cannot be extracted

thereby resulting in an incomplete watermark. For compression attacks

the resulting

$${\rm NC}$$

values increase as the attack parameters increase. In the noise attack

the

$${\rm NC}$$

values of watermark obtained under different intensity attacks can reach more than 0.98

which shows that the algorithm has a great advantage in resisting noise attack. The embedding of the 32×32 watermark image exhibits a similar watermark effect when attacked by the same attacks

and the general trend of

$${\rm NC}$$

values under attacks is the same. The two groups of attack experiments show watermark

$${\rm NC}$$

values greater than 0.94

which indicates that the algorithm can effectively resist some bad attacks.

Conclusion

2

The feature points extracted by SIFT have a certain stability

and the local characteristics of watermarked images are affected by various geometric attacks

such as rotation and shear. The location of a watermark embedded in the algorithm is a part of the feature points extracted from the original image and is scattered. As a result

the watermark is not visually significant. SIFT is adopted to realize locally embedded image blocks. The SVD algorithm optimizes the watermarkembedding performance to extract stable feature points. The FOA allows each experiment group to yield adaptive intensity parameters and achieve the best embedding effect without the need to compare the parameters. This algorithm intelligently searches for optimal solutions that balance the invisibility and robustness of watermarks.