Showing papers on "Modified Huffman coding published in 2020"
TL;DR: This work presents the first- of- its- kind approach addressing Elliptic Curve Cryptography (ECC) to encrypt and decrypt the images to enhance their security during transmission via Single Carrier Frequency Division Multiple Access (SC-FDMA) communication systems.
Abstract: The recent advancements in the internet technology have created the urgency in developing critical data security framework around the globe. One of the most shared multimedia objects is the image which is safeguarded through a task called image encryption. An integrated approach to image encryption is the need of the hour which can combine algorithm and communication model. In this context, this work presents the first- of- its- kind approach addressing Elliptic Curve Cryptography (ECC) to encrypt and decrypt the images to enhance their security during transmission via Single Carrier Frequency Division Multiple Access (SC-FDMA) communication systems. The uniqueness of this work is to combine the encryption scheme and subsequent wireless transmission. Modified Huffman coding has been employed to achieve compression. The viability of the proposed approach was tested and the performance metrics namely Entropy, PSNR, Histogram, correlation coefficient, differential attack, NIST test, and occulation attack analyses were evaluated. The simulation results prove the efficiency of the proposed integrated encryption – compression – communication schema.
23 citations
TL;DR: A model-based deep encoding method built upon Huffman coding to compress a DNN model transmitted through the USB interface to edge devices to increase the USB transmission efficiency for quantized DNN models while reducing the computational cost entailed by the coding process.
Abstract: With the advance of deep neural networks (DNNs), artificial intelligence (AI) has been widely applied to various applications in our daily lives. These DNN-based models can be stored in portable storage disks or low-power Neural Compute Sticks. They can then be deployed in edge devices through the USB interface for AI-based applications, such as Automatic Diagnosis Systems or Smart Surveillance Systems, which provides solutions to incorporating AI into the Internet of Things (IoT). In this work, based on our observation and careful analysis, we propose a model-based deep encoding method built upon Huffman coding to compress a DNN model transmitted through the USB interface to edge devices. Based on the proposed lopsidedness estimation approach, we can exploit a modified Huffman coding method to increase the USB transmission efficiency for quantized DNN models while reducing the computational cost entailed by the coding process. We conducted experiments on several benchmarking DNN models compressed using three emerging quantization techniques, which indicates that our method can achieve a high compression ratio of 88.72%, with 93.76% of the stuffing bits saved on average.
1 citations