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Iris recognition has become one of the most prominent biometrics due to its high recognition accuracy, and it is suitable for the verification and identification systems. Most iris recognition systems can recognize human identity especially when the iris region is free from noise. However, the iris region is typically occluded by noise, eyelid and eyelash. The occluded iris can hide rich iris features, which consists fewer iris features in the iris region, thus reducing the recognition accuracy of the system. The occluded iris is also called a non-ideal iris. In this study, an improvement is made to enhance the performance of an existing sub-iris technique. This can overcome the problem of obtaining iris features from the non-ideal iris. Moreover, the proposed method can also reduce the execution time of the iris recognition system. According to the results, the proposed method managed to localize the sub-iris region, while achieving an equal error rate of 3.75%; area under curve of 0.9832; execution time of 0.719 s; and decidability of 2.9332. These metrics are superior to those of other methods.

Protection of multimedia information from different types of attackers has become important for people and governments. A high definition image has a large amount of data, and thus, keeping it secret is difficult. Another challenge that security algorithms must face with respect to high definition images in medical and remote sensing applications is pattern appearances, which results from existing regions with high density in the same color, such as background regions. An encryption and hiding based new hybrid image security systems are proposed in this paper for the purpose of keeping high definition images secret. First, one hiding method and two encryption methods are used in two hybrid algorithms. The new hiding algorithm proposed here starts by applying reordering and scrambling operations to the six Most Significant Bit planes of the secret image, and then, it hides them in an unknown scene cover image using adding or subtracting operations. Second, two different ciphering algorithms are used to encrypt the stego-image to obtain two different hybrid image security systems. The first encryption algorithm is based on binary code decomposition, while the second algorithm is a modification of an advanced encryption standard. After evaluating each hybrid algorithm alone, a comparison between the two hybrid systems is introduced to determine the best system. Several parameters were used for the performance, including the visual scene, histogram analysis, entropy, security analysis, and execution time.

Currently, the use of telephone systems is highly popular in speech telecommunication and also they have a wide presence in transmission of digital signals. In recent years, audio-speech watermarking as a modern technique makes use of the telephone channel to transmit the watermark data, but addressing the watermark’s vulnerability to noise in the telephone channel is a difficult problem in digital audio-speech watermarking. This paper presents a robust audio-speech watermarking scheme that operates against channel noise according to audio-speech specifications, the channel coding and channel equalizer technique. The proposed scheme is based on the spread spectrum technique and a psychoacoustic model with use of the channel equalizer to remove or minimize the effects of channel noise over the watermark signal. The simulation results for imperceptibility of the watermark, voice quality, and robustness against channel noise are presented in this study. The results indicate that the proposed scheme is highly satisfactory for the cases mentioned above.

In this article, a high-speed and highly restricted encryption algorithm is proposed to cipher high-definition (HD) images based on the modified advanced encryption standard (AES) algorithm. AES is a well-known block cipher algorithm and has several advantages, such as high-level security and implementation ability. However, AES has some drawbacks, including high computation costs, pattern appearance, and high hardware requirements. The aforementioned problems become more complex when the AES algorithm ciphers an image, especially HD images. Three modifications are proposed in this paper to improve AES algorithm performance through, decreasing the computation costs, decreasing the hardware requirements, and increasing the security level. First, modification was conducted using MixColumn transformation in 5 rounds instead of 10 rounds in the original AES-128 to decrease the encryption time. Security is enhanced by improving the key schedule operation by adding MixColumn transformation to this operation as second modification. In addition, to decrease the hardware requirements, S-box and Inv. S-box in the original AES are replaced by one simple S-box used for encryption and decryption in the proposed method. The proposed AES version conducts one of the ciphering modes to solve the appearance pattern problem. Experimental results indicate that the proposed modifications to the AES algorithm made the algorithm more compatible with HD image encryption.

Segmenting iris from images captured under the visible wavelength spectrum represents a challenging task. To address this problem, a new iris segmentation method based on the Circular Gabor Filter (CGF) and optimization is proposed. The CGF is utilized to localize the rough position of the pupil center. According to the localization result, few pixels of the pupil (iris) and non-pupil (non-iris) regions are set as initial pixels in the related region. Next, by optimization scheme, initial pixels are automatically propagated over the image until they cover the whole pupil (iris) and non-pupil (non-iris) regions. The originality of the proposed method includes developing a new method to localize the iris and eyelids boundaries simultaneously, and furthermore robust to reflections. Experimental results on the non-cooperative UBIRIS.v1 iris images reveal the effectiveness of the proposed method in comparison with some of state-of-the-art methods. The results include classification error rates of 2.08 and 3.05 % for session one and two of the UBIRIS.v1 database, respectively.