Biblio

Watermarking is one of the most common data hiding techniques for multimedia elements. Broadcasting, copy control, copyright protection and authentication are the most frequently used application areas of the watermarking. Secret data can be embedded into the cover image with changing the values of the pixels in spatial domain watermarking. In addition to this method, cover image can be converted into one of the transformation such as Discrete Wavelet Transformation (DWT), Discrete Cousin Transformation (DCT) and Discrete Fourier Transformation (DFT). Later on watermark can be embedded high frequencies of transformation coefficients. In this work, cover image transformed one, two and three level DWT decompositions. Binary watermark is hided into the low and high frequencies in each decomposition. Experimental results show that watermarked image is robust, secure and resist against several geometric attacks especially JPEG compression, Gaussian noise and histogram equalization. Peak Signal-to-Noise Ratio (PSNR) and Similarity Ratio (SR) values show very optimal results when we compare the other frequency and spatial domain algorithms.

Digital watermarking is the multimedia leading security protection as it permanently escorts the digital content. Image copyright protection is becoming more anxious as the new 5G technology emerged. Protecting images with a robust scheme without distorting them is the main trade-off in digital watermarking. In this paper, a watermarking scheme based on discrete cosine transform (DCT) and singular value decomposition (SVD) using canny edge detector technique is proposed. A binary encrypted watermark is reshaped into a vector and inserted into the edge detected vector from the diagonal matrix of the SVD of DCT DC and low-frequency coefficients. Watermark insertion is performed by using an edge-tracing mechanism. The scheme is evaluated using the Peak Signal to Noise Ratio (PSNR) and Normalized Correlation (NC). Attained results are competitive when compared to present works in the field. Results show that the PSNR values vary from 51 dB to 55 dB.

Surveillance systems are widely used in airports, streets, banks, military areas, borders, hospitals, and schools. There are two types of surveillance systems which are real-time systems and offline surveillance systems. Usually, security people track videos on time in monitoring rooms to find out abnormal human activities. Real-time human tracking from videos is very expensive especially in airports, borders, and streets due to the huge number of surveillance cameras. There are a lot of research works have been done for automated surveillance systems. In this paper, we presented a new surveillance system to recognize human activities from several cameras using machine learning algorithms. Sequences of images are collected from cameras using the internet of things technology from indoor or outdoor areas. A feature vector is created for each recognized moving object, then machine learning algorithms are applied to extract moving object activities. The proposed abnormal event detection system gives very promising results which are more than 96% accuracy in Multilayer Perceptron, Iterative Classifier Optimizer, and Random Forest algorithms.