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Advanced image and video processing abilities in smart phones and digital cameras make them popular means to capture multimedia. In addition, the integration of internet into such devices users seek to capture and easily share multimedia right from their smartphone while most steganography techniques are computer based. Hence, it is of utmost importance that the multimedia be processed for steganography right within the devices for multimedia authentication.
In this thesis, we first implement steganography into mobile smart devices that can capture multimedia. For devices such as smart phones, we propose a method to hide payload bits within video frames. The solution takes relatively less time and memory to process as opposed to existing computer based solutions. This is a major achievement over traditional techniques that have longer running times leading to power inefficiencies. The idea proposed is to divide the video frames being processed into smaller blocks and perform embedding at block levels, thus localizing any processing that is to be performed.
Simulation results show that the solution proposed can perform about 60 percent faster and 40 percent BER improvement than conventional approach of video steganography.
This thesis takes the foregoing solution to a greater height by using the same algorithm for steganography within Image Sensor Pipeline in digital cameras. The objective behind this is to ensure all images generated from all forms of digital cameras are watermarked automatically. The solutions that exist now are largely dependent on extraction of camera component information. The proposed steganography technique is image centric and aims to resolve existing issues in areas such as image source identification, discrimination of synthetic images and basic image forgery.
After experiments, Peak Signal to Noise Values with a least value of 70 dB even for the worst compression quality (Q) factor of 50 shows how the perceptual quality of the image is preserved. Bit Error Rate of about 5 % for the same quality (Q=50) puts light on the robustness of the technique against JPEG compression.
Advisor: Dongming Peng