Spoof attack by replicating biometric traits represents a real threat to an automatic biometric verification/ authentication system. This is because the system, originally designed to distinguish between genuine users from impostors, simply cannot distinguish between a replicated biometric sample (replica) from a live sample. An effective solution is to obtain some measures that can indicate whether or not a biometric trait has been tempered with, e.g., liveness detection measures. These measures are referred to as evidence of spoofing or anti-spoofing measures. In order to make the final accept/rejection decision, a straightforward solution to define two thresholds: one for the anti-spoofing measure, and another for the verification score. We compared two variants of a method that relies on applying two thresholds – one to the verification (matching) score and another to the anti-spoofing measure. Our experiments carried out using a signature database as well as by simulation show that both the brute-force and its probabilistic variant turn out to be optimal under different operating conditions.
A spatial-spectral metric learning (SSML) framework for hyperspectral image (HSI) classification is proposed. SSML learns a metric by considering both the spectral characteristics and spatial features represented as the mean of neighboring pixels. It first performs the local pixel neighborhood preserving embedding (LPNPE) to reduce the dimensionality of HSI and meanwhile to preserve the spatial local similarity structure. Then, it learns a spectral and spatial distance metric, separately. Finally, the combination of the spectral and spatial metrics yields a joint spatial-spectral metric. It is followed by a nearest neighbor (NN) classifier for HSI classification. SSML shows good performance over the spectral and spatial NN and SVM on the benchmark hyperspectral data set of Indian Pines.
Unsharp masking is an effective enhancement tool to improve the visual quality of fine details in images. However, it also amplifies noisy and over-enhances steep edges. To address this problem, this paper proposes a parametric rational unsharp masking. It utilizes the horizontal and vertical gain factors to enhance image details in two directions independently. Experiments and comparisons are provided to demonstrate its excellent enhancement performance.
Existing collage steganographic methods suffer from low payload of embedding messages. To improve the payload while providing a high level of security protection to messages, this paper introduces a new collage steganographic algorithm using cartoon design. It embeds messages into the least significant bits (LSBs) of color cartoon objects, applies different permutations to each object, and adds objects to a cartoon cover image to obtain the stego image. Computer simulations and comparisons demonstrate that the proposed algorithm shows significantly higher capacity of embedding messages compared with existing collage steganographic methods.
This paper introduces a novel parameter-control framework to produce many new one-dimensional (1D) chaotic maps. It has a simple structure and consists of two 1D chaotic maps, in which one is used as a seed map while the other acts as a control map that controls the parameter of the seed map. Examples and analysis results show that these newly generated chaotic maps have more complex structures and better chaos performance than their corresponding seed and control maps.
Most existing image encryption algorithms often transfer the original image into a noise-like image which is an apparent visual sign indicating the presence of an encrypted image. Motivated by the data hiding technologies, this paper proposes a novel concept of image encryption, namely transforming an encrypted original image into another meaningful image which is the final resulting encrypted image and visually the same as the cover image, overcoming the mentioned problem. Using this concept, we introduce a new image encryption algorithm based on the wavelet decomposition. Simulations and security analysis are given to show the excellent performance of the proposed concept and algorithm.
Technologies and applications of the field-programmable gate array (FPGAs) and digital signal processing (DSP)
require both new customizable number systems and new data formats. This paper introduces a new class of
parameterized number systems, namely the generalized Phi number system (GPNS). By selecting appropriate
parameters, the new system derives the traditional Phi number system, binary number system, beta encoder, and other
commonly used number systems. GPNS also creates new opportunities for developing customized number systems,
multimedia security systems, and image decomposition and enhancement systems. A new image enhancement algorithm
is also developed by integrating the GPNS-based bit-plane decomposition with Parameterized Logarithmic Image
Processing (PLIP) models. Simulation results are given to demonstrate the GPNS's performance.
Histogram equalization is one of the common tools for improving contrast in digital photography, remote sensing,
medical imaging, and scientific visualization. It is a process for recovering lost contrast in an image by remapping the
brightness values in such a way that equalizes or more evenly distributes its brightness values. However, Histogram
Equalization may significantly change the brightness of the entire image and generate undesirable artifacts. Therefore,
many Histogram Equalization based algorithms have been developed to overcome this problem. This paper presents a
comprehensive review study of Histogram Equalization based algorithms. Computer simulations and analysis are
provided to compare the enhancement performance of several Histogram Equalization based algorithms. A secondderivative-
like enhancement measure is introduced to quantitatively evaluate their performance for image enhancement.
This paper introduces a new effective and lossless image encryption algorithm using a Sudoku Matrix to scramble and
encrypt the image. The new algorithm encrypts an image through a three stage process. In the first stage, a reference
Sudoku matrix is generated as the foundation for the encryption and scrambling processes. The image pixels' intensities
are then changed by using the reference Sudoku matrix values, and then the pixels' positions are shuffled using the
Sudoku matrix as a mapping process. The advantages of this method is useful for efficiently encrypting a variety of
digital images, such as binary images, gray images, and RGB images without any quality loss. The security keys of the
presented algorithm are the combination of the parameters in a 1D chaotic logistic map, a parameter to control the size of
Sudoku Matrix and the number of iteration times desired for scrambling. The possible security key space is extremely
large. The principles of the presented scheme could be applied to provide security for a variety of systems including
image, audio and video systems.
Baggage scanning systems are used for detecting the presence of explosives and other prohibited items in baggage at
security checkpoints in airports. However, the CT baggage images contain projection noise and are of low resolution.
This paper introduces a new enhancement algorithm combining alpha-weighted mean separation and histogram
equalization to enhance the CT baggage images while removing the background projection noise. A new enhancement
measure is introduced for quantitative assessment of image enhancement. Simulations and a comparative analysis are
given to demonstrate the new algorithm's performance.
This paper introduces a new recursive sequence called the truncated P-Fibonacci sequence, its corresponding binary
code called the truncated Fibonacci p-code and a new bit-plane decomposition method using the truncated Fibonacci pcode.
In addition, a new lossless image encryption algorithm is presented that can encrypt a selected object using this
new decomposition method for privacy protection. The user has the flexibility (1) to define the object to be protected as
an object in an image or in a specific part of the image, a selected region of an image, or an entire image, (2) to utilize
any new or existing method for edge detection or segmentation to extract the selected object from an image or a specific
part/region of the image, (3) to select any new or existing method for the shuffling process. The algorithm can be used in
many different areas such as wireless networking, mobile phone services and applications in homeland security and
medical imaging. Simulation results and analysis verify that the algorithm shows good performance in object/image
encryption and can withstand plaintext attacks.
This paper presents a new concept of image encryption which is based on edge information. The basic idea is to
separate the image into the edges and the image without edges, and encrypt them using any existing or new encryption
algorithm. The user has the flexibility to encrypt the edges or the image without edges, or both of them. In this manner,
different security requirements can be achieved. The encrypted images are difficult for unauthorized users to decode,
providing a high level of security. We also introduce a new lossless encryption algorithm using 3D Cat Map. This
algorithm can fully encrypt 2D images in a straightforward one-step process. It simultaneously changes image pixel
locations and pixel data. Experimental examples demonstrate the performance of the presented algorithm in image
encryption. It can also withstand chosen-plaintext attack. The presented encryption approach can encrypt all 2D and 3D
images and easily be implemented in mobile devices.
Multimedia scrambling technologies ensure that multimedia content is only used by authorized users by transforming
multimedia data into an unintelligible format. This paper introduces a new P-recursive sequence and two multimedia
scrambling algorithms based on the P-recursive sequence. The P-recursive sequence is a more generalized sequence
which can derive many well-known sequences such as the P-Fibonacci sequence, the P-Lucas sequence and P-Gray
code. The algorithms can be used to scramble two or three dimensional multimedia data in one step. Electronic
signatures, grayscale images and three-color-component images are all examples of 2-D and 3-D multimedia data which
can utilize these algorithms. Furthermore, a security key parameter p may be chosen as different or the same values for
each dimensional component of the multimedia data. Experiments show that the presented algorithms can scramble
multimedia data at different levels of security by partially or fully encrypting multimedia data. They also have been
demonstrated in the experiments to show good performance in known-plain text attack and common image attacks such
as data loss, Gaussian noise, and Salt Pepper noise. The scrambled multimedia data can be completely reconstructed
only by using the correct security keys.
Image scrambling is used to make images visually unrecognizable such that unauthorized users have difficulty decoding
the scrambled image to access the original image. This article presents two new image scrambling algorithms based on
Fibonacci p-code, a parametric sequence. The first algorithm works in spatial domain and the second in frequency
domain (including JPEG domain). A parameter, p, is used as a security-key and has many possible choices to guarantee
the high security of the scrambled images. The presented algorithms can be implemented for encoding/decoding both in
full and partial image scrambling, and can be used in real-time applications, such as image data hiding and encryption.
Examples of image scrambling are provided. Computer simulations are shown to demonstrate that the presented
methods also have good performance in common image attacks such as cutting (data loss), compression and noise. The
new scrambling methods can be implemented on grey level images and 3-color components in color images. A new
Lucas p-code is also introduced. The scrambling images based on Fibonacci p-code are also compared to the scrambling
results of classic Fibonacci number and Lucas p-code. This will demonstrate that the classical Fibonacci number is a
special sequence of Fibonacci p-code and show the different scrambling results of Fibonacci p-code and Lucas p-code.