Aboul Ella Hassanien (Abo)

In recent years image authentication has gained substantial attraction by the research community. It promises the solution to many problems such as content piracy, illicit manipulation of medical/legal documents, content security and so on. As watermark-based image authentication approaches are efficient and attractive, some types of watermarks such as logos, labels, trademark, or random sequence representing the author’s ownership, are mbedded into the desired digital image. Generally, a registration to the authentication center is necessary, which helps to solve ownership disputes by identifying the owner of the disputed media. If necessary, the embedded watermark in the digital image can be used to verify ownership Due to the open environment of Internet downloading, copyright protection introduces a new set of challenging problems regarding security and illegal distribution of privately owned images. One solution to these problems is digital watermarking, i.e., the insertion of information into the image data in such a way that the added information is not visible and yet resistant to image alterations. A watermarking technique is to prevent digital images that belong to rightful owners from being illegally commercialized or used, and it can verify the intellectual property right. The watermark should be robust and transparent, but the ways of pursuing transparency and robustness are conflict. For instance, if we would like to concentrate on the transparency issue, it is natural to embed the smallest modulation into images whenever possible. However, due to such small values in the embedded watermark, attacks can easily destroy the problems The first proposed solution is based on the associative watermarking and vector quantization. It achieves more effective against several images processing such as blurring, sharpening adding in Gaussian noise, cropping, and JPEG lossy compression especially in case of Gaussian noise and blurring. Also this technique is implemented to hide biometric data, fingerprint image, over three different types of medical images: CT, MRI and interventional images. It also achieves an effective resistance against several images processing such as JPEG lossy compression, sharpening, blurring and adding in Gaussian noise The second contribution in this thesis is strict authentication of multimodal biometric images using an improved secure hash function (ISHA-1) and near sets. It indicates that the proposed hash function is collision resistant and assures a good compression and preimage resistance. Also it reduces the time of implementation comparing to standard secure hash function. Moreover, the difference in time between SHA-1 and ISHA-1 increases by increasing the number of letters in message since the running time of implementation of ISHA-1 is limited compared to the running time of implementation of SHA-1. Also the proposed approach guarantees the security assurance and reduces the time of implementation. The third proposed contribution is fragile watermarking approach for image authentication based on rough k-means only and hybridization of rough k-means and particle swarm optimization. It can embed watermark without causing noticeable visual artifacts, and does not only achieve superior tamper detection in images accurately, it also recovers tampered regions effectively. In addition, it shows that the proposed approach can effectively thwart different attacks, such as the cut-and paste attack and collage attack, while sustaining superior tamper detection and localization accuracy. Moreover, the running time of implemented hybrid system is limited compared to the running time of the implemented rough k-means only. Especially, when we used exponential particle swarm optimization to optimize the parameters of rough k-means.

This paper proposes an elementary pattern detection approach for viruses propagated through e-mail and address books using the non-uniform pheromone deposition mechanism of ant colony. The local temporary tabu memory has been used to learn the pattern and it can combine known information from past viruses with a type of prediction for future viruses. This is achieved through certain generated test signature of viruses associated with e-mail over landscape. A non-uniform and non-decreasing time function for pheromone deposition and evaporation ensures that subsequent ants who are close enough to a previously selected trial solution will follow the trajectory or test landscape. They are capable to examine gradually thicker deposition of pheromone over the trajectory. It is empirically shown that the proposed modified pheromone learning mechanism can be an alternative approach to detect virus pattern for e-mail messages.

This paper proposes an elementary pattern detection approach for viruses propagated through e-mail and address books using the non-uniform pheromone deposition mechanism of ant colony. The local temporary tabu memory has been used to learn the pattern and it can combine known information from past viruses with a type of prediction for future viruses. This is achieved through certain generated test signature of viruses associated with e-mail over landscape. A non-uniform and non-decreasing time function for pheromone deposition and evaporation ensures that subsequent ants who are close enough to a previously selected trial solution will follow the trajectory or test landscape. They are capable to examine gradually thicker deposition of pheromone over the trajectory. It is empirically shown that the proposed modified pheromone learning mechanism can be an alternative approach to detect virus pattern for e-mail messages.

This paper presents a feature-based retinal image registration schema. A structural feature, namely, bifurcation structure, has been used for the proposed feature-based registration schema. The bifurcation structure is composed of a master bifurcation point and its three connected neighbors. The characteristic vector of each bifurcation structure consists of the normalized branching angle and length, which is invariant against translation, rotation, scaling, and even modest distortion. The proposed schema is composed of five fundamental phases, namely, input retinal images pre-processing, vascular network detection, noise removal, bifurcation points detection in vascular networks, and bifurcation points matching in pairs of retinal images. The effectiveness of the proposed schema is demonstrated by the experiments with 12 pairs retinal images collected from clinical patients. The registration is carried out through optimizing a certain similarity function, namely, normalized correlation of images. It has been observed that the proposed schema has achieved good performance accuracy.

The objective of this book chapter is to present the rough sets and pulse coupled neural network scheme for Ultrasound Biomicroscopy glaucoma images analysis. To increase the efficiency of the introduced scheme, an intensity adjustment process is applied first using the Pulse Coupled Neural Network (PCNN) with a median filter. This is followed by applying the PCNN-based segmentation algorithm to detect the boundary of the interior chamber of the eye image. Then, glaucoma clinical parameters have been calculated and normalized, followed by application of a rough set analysis to discover the dependency between the parameters and to generate set of reduct that contains minimal number of attributes. Finally, a rough confusion matrix is designed for discrimination to test whether they are normal or glaucomatous eyes. Experimental results show that the introduced scheme is very successful and has high detection accuracy.