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Plane transformation visual cryptography takes a unique approach to some of the current shortcomings of current visual cryptography techniques. Typically, the direction and placement of the encrypted shares is critical when attempting to recover the secret. Many schemes are highly dependant on this stacking order. Within this paper, the scheme presented illustrates a technique whereby this restriction is loosened such that the number of acceptable alignment points is increased by performing a simple plane transform on one of the shares. This results in the same secret being recovered when the shares correctly aligned. The technique has also been extended to encompass multiple secrets, each of which can be recovered depending on the type of transformation performed on the shares. [ABSTRACT FROM AUTHOR]

Digital fingerprinting is a technology which aims to embed unique marks with traceability in order to identify users who use their multimedia content for unintended purpose. A cost-efficient attack against digital fingerprinting, known as collusion attack, involves a group of users who combine their fingerprinted content for the purpose of attenuating or removing the fingerprints. In this paper, we analyze and simulate the effect of Gaussian noise with different energies added in the noise-free forgery on both the detection performance of correlation-based detector and the perceptual quality of the attacked content. Based upon the analysis and the principal of informed watermark embedding, we propose a novel collusion attack strategy, self-adaptive noise optimization (SANO) collusion attack. The experimental results, under the assumption that orthogonal fingerprints are used, show that the proposed collusion attack performs more effectively than the most of existed collusion attacks. Less than three pieces of fingerprinted content can sufficiently interrupt orthogonal fingerprints which accommodate many thousands of users. Meanwhile, high fidelity of the attacked content is retained after the proposed collusion attack. [ABSTRACT FROM AUTHOR]

Visual cryptography (VC) is a powerful technique that combines the notions of perfect ciphers and secret sharing in cryptography with that of raster graphics. VC takes a binary image (the secret) and divides it into two or more pieces known as shares. When the shares are printed on transparencies and then superimposed, the secret can be recovered. No computer participation is required, thus demonstrating one of the distinguishing features of VC. VC is a unique technique in the sense that the encrypted message can be decrypted directly by the human visual system (HVS). In this survey, we will summarize the latest developments of visual cryptography since its inception in 1994, introduce the main research topics in this area and outline the current problems and possible solutions. Directions and trends for future VC work shall also be examined along with possible VC applications. [ABSTRACT FROM AUTHOR]

In forensic investigations, it is common for forensic investigators to obtain a photograph of evidence left at the scene of crimes to aid them catch the culprit(s). Although, fingerprints are the most popular evidence that can be used, scene of crime officers claim that more than 30% of the evidence recovered from crime scenes originate from palms. Usually, palmprints evidence left at crime scenes are partial since very rarely full palmprints are obtained. In particular, partial palmprints do not exhibit a structured shape and often do not contain a reference point that can be used for their alignment to achieve efficient matching. This makes conventional matching methods based on alignment and minutiae pairing, as used in fingerprint recognition, to fail in partial palmprint recognition problems. In this paper a new partial-to-full palmprint recognition based on invariant minutiae descriptors is proposed where the partial palmprint΄s minutiae are extracted and considered as the distinctive and discriminating features for each palmprint image. This is achieved by assigning to each minutiae a feature descriptor formed using the values of all the orientation histograms of the minutiae at hand. This allows for the descriptors to be rotation invariant and as such do not require any image alignment at the matching stage. The results obtained show that the proposed technique yields a recognition rate of 99.2%. The solution does give a high confidence to the judicial jury in their deliberations and decision. [ABSTRACT FROM AUTHOR]

Visual cryptography provides a very powerful technique by which one secret can be distributed into two or more pieces known as shares. When the shares are printed on transparencies and then superimposed exactly together, the original secret can be recovered without computer participation. In this paper, we take multiple secrets into consideration and generate a key share for all the secrets; correspondingly, we share each secret using this key share. The secrets are recovered when the key is superimposed on the combined share in different locations using the proposed scheme. Also discussed and illustrated within this paper is how to embed a share of visual cryptography into halftone and colour images. The remaining share is used as a key share in order to perform the decryption. It is also worth noting that no information regarding the secrets is leaked in any of our proposed schemes. We provide the corresponding results in this paper. [ABSTRACT FROM AUTHOR]

This paper introduces a new technique for palmprint recognition based on Fisher Linear Discriminant Analysis (FLDA) of Gabor filter bank. This method involves convolving a palmprint image with a bank of Gabor filters at different scales and rotations for robust palmprint features extraction. Once these feature are extracted, FLDA is applied for dimensionality reduction and class separability. Since the palmprint features are derived from the principal lines, wrinkles and texture along the palm area one should carefully consider this fact when selecting the appropriate palm region for feature extraction process in order to enhance recognition accuracy. To address this problem, an improved region of interest (ROI) extraction algorithm is introduced. This algorithm allows for an efficient extraction of the whole palm area ignoring all the undesirable parts, such as the fingers and background. Experiments have shown that the proposed method yields attractive performances evidence by an Equal Error Rate (EER) of 0.03%. [ABSTRACT FROM AUTHOR]

In this paper we splice together an image which has been split up on a piece of paper by using duplication detection. The nearest pieces are connected using edge searching and matching. For the pieces that have graphics or textures, we seek the matching pieces using the edge shape and intersection between the two near pieces. Thus, the initial step is to mark the direction of each piece and put the pieces that have straight edges to the initial position to determine the profile of the whole image. The other pieces are then fixed into the corresponding position by using the edge information (shape, residual trace and matching) after duplication or sub-duplication detection. In the following steps, the patches with different edge shapes are searched using edge duplication detection. With the reduction of rest pieces, the montage procedure will become easier and faster. [ABSTRACT FROM AUTHOR]