The spread of altered media in the form of fake videos, audios, and images, has been largely increased over the past few years. Advanced digital manipulation tools and techniques make it easier to generate fake content and post it on social media. In addition, tweets with deep fake content make their way to social platforms. The polarity of such tweets is significant to determine the sentiment of people about deep fakes. This paper presents a deep learning model to predict the polarity of deep fake tweets. For this purpose, a stacked bi-directional long short-term memory (SBi-LSTM) network is proposed to classify the sentiment of deep fake tweets. Several well-known machine learning classifiers are investigated as well such as support vector machine, logistic regression, Gaussian Naive Bayes, extra tree classifier, and AdaBoost classifier. These classifiers are utilized with term frequency-inverse document frequency and a bag of words feature extraction approaches. Besides, the performance of deep learning models is analyzed including long short-term memory network, gated recurrent unit, bi-direction LSTM, and convolutional neural network+LSTM. Experimental results indicate that the proposed SBi-LSTM outperforms both machine and deep learning models and achieves an accuracy of 0.92.

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This paper presents a highly efficient AES algorithm resistant to differential power analysis (DPA). This paper conducts a simulation based correlation power analysis (CPA) attack on AES implementation with different structures. The proposed idea does not affect the working frequency and does not alter the algorithm core architecture. A minimal overhead hardware is used to manage the dataflow of plaintext and noise.

As a result of bandwidth and storage limitations, image compression techniques are widely used in data transmission and data storage. In a congested network like the Internet or low bandwidth communication for wireless transmission, image compression at a low bit rate is necessary. In this paper, a very low bit rate image compression scheme is proposed. This scheme is a hybrid method that combines the high compression ratio of Vector Quantization (VQ) with the good energycompaction property of Discrete Cosine Transform (DCT). In order to increase the compression ratio while preserving decent reconstructed image quality, Image is compressed using vector quantization (VQ), while DCT was used the code books block. Simulation results show the effectiveness of the proposed method. INTODUCTIONImage and video transmissions require particularly large bandwidth and storage space. Image and video compression technology is therefore essential to overcome these problems. Image compression is a technique for image data rate reduction to save storage space. In other words, the purpose of image compression is to reduce the amount of data and to achieve low bit rate digital representation without perceived loss of image quality [1].Image compression can be lossy or lossless. Lossless compression is sometimes preferred for artificial images such as technical drawings, icons, or medical images. Lossy methods are especially suitable for natural images such as photos in applications where minor loss of fidelity is acceptable to achieve a substantial reduction in bit rate.There are two well-known major approaches to implement lossy image compression: Vector Quantization (VQ) and Discrete Cosine Transform (DCT) technique. Vector quantization (VQ) is a popular method for image compression. In VQ, limited numbers of vectors (code words) are used to approximate an N-dimensional space. Selecting the code words such that the best representation of the space is obtained is a major issue in VQ [2].The goal of VQ codebook generation is to find an optimal codebook that yields the lowest possible distortion when compared with all other codebooks of the same size. VQ performance is directly proportional to the codebook size and the vector size. According to Shannon's rate distortion theory larger vectors would result in better VQ performance. However, with increased vector size the required codebook size also increase, and that in-turn results in an exponential increase in encoding complexity [3].Another well-known image compression method is Discrete Cosine Transform (DCT), in this method compression is accomplished by applying a linear transform to de-correlate the image data (source encoder), quantizing the resulting transform coefficients (quantizer), and entropy coding the quantized values (entropy encoder). This paper proposes a new image compression scheme based on Combined VQ and DCT to take advantages of both of them.This paper is organized as follows. A brief review of VQ scheme is presented in Section 2. DCT scheme is illustrated in S...

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