StegNet: Mega Image Steganography Capacity with Deep Convolutional Network

Traditional image steganography often leans interests towards safely embedding hidden information into cover images with payload capacity almost neglected. This paper combines recent deep convolutional neural network methods with image-into-image steganography. It successfully hides the same size images with a decoding rate of 98.2% or bpp (bits per pixel) of 23.57 by changing only 0.76% of the cover image on average. Our method directly learns end-to-end mappings between the cover image and the embedded image and between the hidden image and the decoded image. We~further show that our embedded image, while with mega payload capacity, is still robust to statistical analysis.Comment: https://github.com/adamcavendish/StegNet-Mega-Image-Steganography-Capacity-with-Deep-Convolutional-Networ

A novel steganography approach for audio files

We present a novel robust and secure steganography technique to hide images into audio files aiming at increasing the carrier medium capacity. The audio files are in the standard WAV format, which is based on the LSB algorithm while images are compressed by the GMPR technique which is based on the Discrete Cosine Transform (DCT) and high frequency minimization encoding algorithm. The method involves compression-encryption of an image file by the GMPR technique followed by hiding it into audio data by appropriate bit substitution. The maximum number of bits without significant effect on audio signal for LSB audio steganography is 6 LSBs. The encrypted image bits are hidden into variable and multiple LSB layers in the proposed method. Experimental results from observed listening tests show that there is no significant difference between the stego audio reconstructed from the novel technique and the original signal. A performance evaluation has been carried out according to quality measurement criteria of Signal-to-Noise Ratio (SNR) and Peak Signal-to-Noise Ratio (PSNR)

Transparent authentication methodology in electronic education

In the context of on-line assessment in e-learning, a problem arises when a student taking an exam may wish to cheat by handing over personal credentials to someone else to take their place in an exam, Another problem is that there is no method for signing digital content as it is being produced in a computerized environment. Our proposed solution is to digitally sign the participant’s work by embedding voice samples in the transcript paper at regular intervals. In this investigation, we have demonstrated that a transparent stenographic methodology will provide an innovative and practical solution for achieving continuous authentication in an online educational environment by successful insertion and extraction of audio digital signatures