**self similarity attack**: This attack is based on self-similarities of the image. Usually correlation between neighbor pixels is taken into account. With self similarities, it is the correlation between deferent parts of the image which is of interest. The basic idea of the attack consists in substituting some parts of the picture with some other parts of itself which are, or look, similar. The process is presented in _gure 2. The objective is to approximate, to stir the watermark signal while keeping clear the cover signal.

**STATE-OF-THE-ART WATERMARKING ATTACKS**

In a field of digital watermarks, there are various categorizations of attacks on watermarks. In this paper, the categorization presented in [1] is used. The wide class of existing attacks may be divided to four classes of attacks: removal attacks, geometric attacks, cryptographic attacks, and protocol attacks.

**2.1 Removal Attacks**

*Removal attacks*achieve complete removal of the watermark information from the watermarked data without cracking the security of the watermarking algorithm. This category includes denoising , quantization, remodulation , averaging, and collusion attacks. Not all of these methods always come close to complete watermark removal, but they may damage the watermark information significantly.

**2.2 Geometrical Attacks**

*Geometrical attacks*do not remove the embedded watermark itself, but intend to distort the watermark detector synchronization with the embedded information.To this category there belong the cropping, flip, rotation and synchronization removal attacks too.

**2.3 Cryptographic Attacks**

*Cryptographic attacks*aim at cracking the security methods in watermarking schemes and thus finding a way to remove the embedded watermark information or to embed misleading watermarks. One such technique is brute-force search for the embedded secret information. Practically, application of these attacks is restricted due to their high computational complexity.

**2.4 Protocol Attacks**

*Protocol attacks*aim at attacking the entire concept of the watermarking application. One type of protocol attack is the copy attack. The main idea of a copy attack is to copy a watermark from one image to another image without knowledge of the key used for the watermark embedding to create ambiguity with respect to the real ownership of data.

**Speckle Noise:**Speckle noise in SAR is a multiplicative noise, i.e. it is in direct proportion to the local grey level in any area.The signal and the noise are statistically independent of each other.The sample mean and variance of a single pixel are equal to the mean and variance of the local area that is centred on that pixel.

Speckle noise is a granular noise that inherently exists in and degrades the quality of the active radar and synthetic aperture radar (SAR) images

**Gaussian Noise**

Gaussian noise is statistical noise that has its probability density function equal to that of the normal distribution, which is also known as the Gaussian distribution. In other words, the values that the noise can take on are Gaussian-distributed. A special case is white Gaussian noise, in which the values at any pairs of times are statistically independent (and uncorrelated). In applications, Gaussian noise is most commonly used as additive white noise to yield additive white Gaussian noise.Gaussian noise is properly defined as the noise with a Gaussian amplitude distribution. This says nothing of the correlation of the noise in time or of the spectral density of the noise. Labeling Gaussian noise as 'white' describes the correlation of the noise. It is necessary to use the term "white Gaussian noise" to be precise. Gaussian noise is sometimes misunderstood to be white Gaussian noise, but this is not the case.

**Salt and Pepper Noise:**

Salt and pepper noise is a form of noise typically seen on images. It represents itself as randomly occurring white and black pixels.

**Gaussian Blur:**A Gaussian blur (also known as Gaussian smoothing) is the result of blurring an image by a Gaussian function. It is a widely used effect in graphics software, typically to reduce image noise and reduce detail. The visual effect of this blurring technique is a smooth blur resembling that of viewing the image through a translucent screen, distinctly different from the bokeh effect produced by an out-of-focus lens or the shadow of an object under usual illumination. Gaussian smoothing is also used as a pre-processing stage in computer vision algorithms in order to enhance image structures at different scales—see scale-space representation and scale-space implementation.

Mathematically, applying a Gaussian blur to an image is the same as convolving the image with a Gaussian function; this is also known as a two-dimensional Weierstrass transform. By contrast, convolving by a circle (i.e., a circular box blur) would more accurately reproduce the bokeh effect. Since the Fourier transform of a Gaussian is another Gaussian, applying a Gaussian blur has the effect of reducing the image's high-frequency components; a Gaussian blur is thus a low pass filter.

Attacks on Digital Images - Digital Image Processing
Reviewed by Suresh Bojja
on
8/25/2015 09:01:00 AM
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