Skema Penyembunyian Teks Terkompresi Arithmetic Coding pada Citra Digital Manggunakan Kuantisasi Berbasis Graf

Dewasa ini, komunikasi melalui aplikasi jaringan dan internet membutuhkan keamanan dari serangan cyber, terutama komunikasi yang melibatkan transmisi data [15]. Tidak sedikit algoritma penyembunyian dan pengamanan data telah dikembangkan untuk mengatasi penyadapan. Cryptography, watermarking, dan steganography adalah tiga teknik utama yang paling sering digunakan untuk penyembunyian data dalam menjaga keamanan data saat tranmisi atau komunikasi digital dilakukan. Cryptography menekankan pada proteksi data dengan cara mengenkripsi data rahasia yang dimaksud. Watermarking dan steganography memiliki kesamaan dalam menyembunyikan data di dalam suatu media digital. Dimana watermarking lebih mementingkan keaslian dari medianya untuk melindungi hak cipta, dan steganography bertujuan untuk menjaga data rahasia yang disisipkan. Jadi, yang membedakan steganografi dengan metode yang lain adalah tujuannya yang mengutamakan pada data rahasia yang disisipkan, ketahanan terhadap metode – metode pemprosesan image dan kompresi, dan kapasitas dari data rahasia yang dapat disembunyikan di dalam cover image [3]. Penelitian ini mengimplementasikan skema kuantisasi berbasis graf yaitu sebuah skema penyisipan yang direpresentasikan pada suatu graf. Skema ini memperhatikan kapasitas penyisipan data dengan memanfaatkan algoritma Arithmetic Coding untuk kompresi teks yang akan disisipi dan memperhatikan nilai PSNR citra tersisipi. Skema ini memberikan performansi relatif baik dengan nilai PSNR 28,5324 db yang berhasil menyisipkan data sebanyak 7255

Skema Penyembunyian Teks Terkompresi Adaptive Huffman pada Citra Digital Menggunakan Kuantisasi Berbasis Graf

Jaringan komputer dan internet semakin banyak digunakan untuk aktivitas pengiriman data. Namun, tidak ada jaminan bahwa jaringan komputer dan internet yang digunakan sebagai media pengiriman data ini aman dari pihak ketiga yang tidak memiliki hak akses terhadap data tersebut [1]. Berbagai teknik telah dikembangkan untuk melindungi data dari pengaksesan secara ilegal. Salah satu diantaranya yaitu dengan menyisipkan/ menyembunyikan data tersebut ke dalam media cover. Pada penelitian ini, implementasi penyembunyian data memanfaatkan kuantisasi berbasis graf, yaitu menggunakan Vector Quantization (VQ) dan pewarnaan graf dengan menggunakan Genetic Algorithm. Untuk meningkatkan kapasitas penyisipan, data dikompres terlebih dahulu dengan menggunakan Adaptive Huffman sebelum penyisipan dilakukan. Hasil pengujian menunjukkan bahwa skema ini dapat menghasilkan kapasitas penyisipan sebanyak 9000 bit atau sekitar 1800 karakter, dengan nilai PSNR 27,5054 db. Keywords: Penyembunyian Data, Kuantisasi Berbasis Graf, Vector Quantization, Pewarnaan Graf, Adaptive Huffman, Genetic Algorith

Large-capacity and Flexible Video Steganography via Invertible Neural Network

Video steganography is the art of unobtrusively concealing secret data in a cover video and then recovering the secret data through a decoding protocol at the receiver end. Although several attempts have been made, most of them are limited to low-capacity and fixed steganography. To rectify these weaknesses, we propose a Large-capacity and Flexible Video Steganography Network (LF-VSN) in this paper. For large-capacity, we present a reversible pipeline to perform multiple videos hiding and recovering through a single invertible neural network (INN). Our method can hide/recover 7 secret videos in/from 1 cover video with promising performance. For flexibility, we propose a key-controllable scheme, enabling different receivers to recover particular secret videos from the same cover video through specific keys. Moreover, we further improve the flexibility by proposing a scalable strategy in multiple videos hiding, which can hide variable numbers of secret videos in a cover video with a single model and a single training session. Extensive experiments demonstrate that with the significant improvement of the video steganography performance, our proposed LF-VSN has high security, large hiding capacity, and flexibility. The source code is available at https://github.com/MC-E/LF-VSN.Comment: Accepted by CVPR 202

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Data Hiding and Its Applications

Data hiding techniques have been widely used to provide copyright protection, data integrity, covert communication, non-repudiation, and authentication, among other applications. In the context of the increased dissemination and distribution of multimedia content over the internet, data hiding methods, such as digital watermarking and steganography, are becoming increasingly relevant in providing multimedia security. The goal of this book is to focus on the improvement of data hiding algorithms and their different applications (both traditional and emerging), bringing together researchers and practitioners from different research fields, including data hiding, signal processing, cryptography, and information theory, among others

DCT Implementation on GPU

There has been a great progress in the field of graphics processors. Since, there is no rise in the speed of the normal CPU processors; Designers are coming up with multi-core, parallel processors. Because of their popularity in parallel processing, GPUs are becoming more and more attractive for many applications. With the increasing demand in utilizing GPUs, there is a great need to develop operating systems that handle the GPU to full capacity. GPUs offer a very efficient environment for many image processing applications. This thesis explores the processing power of GPUs for digital image compression using Discrete cosine transform