اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدA novel method of speech information hiding based on 3D-Magic Matrix
155
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Redundant information of low-bit-rate speech is extremely small, thus its very difficult to implement large capacity steganography on the low-bit-rate speech. Based on multiple vector quantization characteristics of the Line Spectrum Pair (LSP) of the speech codec, this paper proposes a steganography scheme using a 3D-Magic matrix to enlarge capacity and improve quality of speech. A cyclically moving algorithm to construct a 3D-Magic matrix for steganography is proposed in this paper, as well as an embedding and an extracting algorithm of steganography based on the 3D-Magic matrix in low-bit-rate speech codec. Theoretical analysis is provided to demonstrate that the concealment and the hidden capacity are greatly improved with the proposed scheme. Experimental results show the hidden capacity is raised to 200bps in ITU-T G.723.1 codec. Moreover, the quality of steganography speech in Perceptual Evaluation of Speech Quality (PESQ) reduces no more than 4%, indicating a little impact on the quality of speech. In addition, the proposed hidden scheme could prevent being detected by some steganalysis tools effectively.
قيم البحث
اقرأ أيضاً
Reversible data hiding in encrypted images is an eff ective technique for data hiding and preserving image privacy. In this paper, we propose a novel schema based on polynomial arithmetic, which achieves a high embedding capacity with the perfect rec
overy of the original image. An effi cient two-layer symmetric en- cryption method is applied to protect the privacy of the original image. One polynomial is generated by the encryption key and a group of the encrypted pixel, and the secret data is mapped into another polynomial. Through the arithmetic of these two polynomials, the purpose of this work is achieved. Fur- thermore, pixel value mapping is designed to reduce the size of auxiliary data, which can further improve embedding capacity. Experimental results demon- strate that our solution has a stable and good performance on various images. Compared with some state-of-the-art methods, the proposed method can get better decrypted image quality with a large embedding capacity.
Data hiding is the art of concealing messages with limited perceptual changes. Recently, deep learning has provided enriching perspectives for it and made significant progress. In this work, we conduct a brief yet comprehensive review of existing lit
erature and outline three meta-architectures. Based on this, we summarize specific strategies for various applications of deep hiding, including steganography, light field messaging and watermarking. Finally, further insight into deep hiding is provided through incorporating the perspective of adversarial attack.
Data hiding is referred to as the art of hiding secret data into a digital cover for covert communication. In this letter, we propose a novel method to disguise data hiding tools, including a data embedding tool and a data extraction tool, as a deep
neural network (DNN) with an ordinary task. After training a DNN for both style transfer and data hiding, while the DNN can transfer the style of an image to a target one, it can be also used to hide secret data into a cover image or extract secret data from a stego image by inputting the trigger signal. In other words, the tools of data hiding are hidden to avoid arousing suspicion.
With the rapid increase in software exploits, the last few decades have seen several hardware-level features to enhance security (e.g., Intel MPX, ARM TrustZone, Intel SGX, Intel CET). Due to security, performance and/or usability issues these featur
es have attracted steady criticism. One such feature is the Intel Memory Protection Extensions (MPX), an instruction set architecture extension promising spatial memory safety at a lower performance cost due to hardware-accelerated bounds checking. However, recent investigations into MPX have found that is neither as performant, accurate, nor precise as cutting-edge software-based spatial memory safety. As a direct consequence, compiler and operating system support for MPX is dying, and Intel has begun to manufacture desktop CPUs without MPX. Nonetheless, given how ubiquitous MPX is, it provides an excellent yet under-utilized hardware resource that can be aptly salvaged for security purposes. In this paper, we propose Simplex, a library framework that re-purposes MPX registers as general purpose registers. Using Simplex, we demonstrate how MPX registers can be used to store sensitive information (e.g., encryption keys) directly on the hardware. We evaluate Simplex for performance and find that its overhead is small enough to permit its deployment in all but the most performance-intensive code. We refactored the string.h buffer manipulation functions and found a geometric mean 0.9% performance overhead. We also modified the deepsjeng and lbm SPEC CPU2017 benchmarks to use Simplex and found a 1% and 0.98% performance overhead respectively. Finally, we investigate the behavior of the MPX context with regards to multi-process and multi-thread programs.
A new method for analyzing the returns of the custom-made micro-LIDAR system, which is operated along with the two MAGIC telescopes, allows to apply atmospheric corrections in the MAGIC data analysis chain. Such corrections make it possible to extend
the effective observation time of MAGIC under adverse atmospheric conditions and reduce the systematic errors of energy and flux in the data analysis. LIDAR provides a range-resolved atmospheric backscatter profile from which the extinction of Cherenkov light from air shower events can be estimated. Knowledge of the extinction can allow to reconstruct the true image parameters, including energy and flux. Our final goal is to recover the source-intrinsic energy spectrum also for data affected by atmospheric extinction from aerosol layers, such as clouds.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
