Secure image hiding in speech signal by steganography-mining and encryption

<span>Information hiding techniques are constantly evolving due to the increased need for security and confidentiality. This paper proposes a working mechanism in three phases. The first phase includes scrambling the values of the gray image depending on a series of keys that are generated using a quantum chaotic map. The second phase generates hybrid keys by mixing a Zaslavsky and a 3D Hanon map that are used to encrypt the gray image values produced after the scramble. Finally, in the third phase, a new algorithm is suggested to hide the encrypted gray image at random locations within a speech file. This algorithm includes the LSB algorithm to determine the hidden bits and the zero-crossing K-means algorithm in selecting locations mining in a scattered manner so that hackers cannot easily retrieve the hidden data of any hacked person. Also used a fractional fourier transform to choose magnitude value as specific data to hide encoded image data. The measures MSE, PSNR, NSCR, and UACI are using to measure the work efficiency in the encryption algorithm, and in measuring the efficiency of the hidden algorithm, use the measures SNR, PSNR, and MSE. The results of the paper are encouraging and efficient compared to other algorithms that performed the same work. Hence our results show the larger the image dimensions used, the better the values.</span>

Drowsy Driver Detection using MSER Feature Detection and Binarization on MATLAB

Every year, 1.5 lakh people die in road mishaps in India. Among these accidents, 40% are due to ‘Drowsy or Sleep Driving’. According to several statistics, almost all commercial private drivers tend to drive continuously for 10 hours a day. Nearly all road accidents caused due to lack of sleep and drowsiness are highly hazardous and fatal. Drowsy Driver Detection Algorithm acquires real-time video and captures snapshots using an external Webcam. Using the Viola-Jones Algorithm, the face and the eyes of the driver are detected. The original RGB eye image is converted to a Gray image and then into a Binary image. Two techniques, Maximally Stable Extremal Regions Feature Detection and Binarization are deployed to determine the status of the driver. This research paper focuses on the development of a MATLAB algorithm to alert the driver or the co-passenger on-time by plotting the MSER Features and thresholding the acquired real-time images.