Compression Encryption Research Articles

Pulsation artifact reduction using compressed sensitivity encoding in Gd-EOB-DTPA contrast-enhanced magnetic resonance imaging.

In Gd-EOB-DTPA-enhanced MRI, cardiac pulsation artifacts in the left lobe often hinder diagnosis, the image quality need to improve. This study aimed to reduce cardiac pulsation artifacts in Gd-EOB-DTPA-enhanced three-dimensional (3D) T1-weighted turbo-field echo (3D-T1TFE) using compressed sensitivity encoding (CS).For phantom evaluation, the cardiac phantom was manually operated using a metronome-synchronized apparatus, comprising a bag-valve mask, a breathing circuit, and a Jackson-Rees system. Transverse images of a liver phantom were acquired using enhanced T1 high-resolution isotropic volumetric excitation with CS (CS-eTHRIVE) and sensitivity encoding (S-eTHRIVE). For evaluation, images obtained during cardiac phantom operation were subtracted from those obtained when the phantom was stationary. Standard deviation (SD) of the difference images was used as the evaluation metric, and assessments were conducted based on changes in heart rate and TFE factor. For clinical image evaluation, artifacts in hepatobiliary phase images acquired 15min after Gd-EOB-DTPA injection in the order of S-eTHRIVE and CS-eTHRIVE were visually evaluated at four levels. In heart-rate evaluation (40, 60, and 80 beats/min), CS-eTHRIVE revealed significantly lower SD values compared to S-eTHRIVE across all heart rates (P < 0.01), with no significant differences between heart rates. For TFE factor evaluation, CS-eTHRIVE with a factor of 35 exhibited the lowest SD, which was significantly different from all other groups (P < 0.01). In clinical image evaluation, CS-eTHRIVE achieved higher visual scores (mean ± SD: 3.72 ± 0.46) compared with S-eTHRIVE (2.72 ± 0.98, P < 0.01).CS reduced pulsation artifacts in Gd-EOB-DTPA-enhanced 3D-T1TFE.

Multi-medical image protection: compression–encryption scheme based on TLNN and mask cubes

Multi-medical image protection: compression–encryption scheme based on TLNN and mask cubes

Retraction Note: An effective text compression–encryption using tight and indirect encryptions

Retraction Note: An effective text compression–encryption using tight and indirect encryptions

Image compression encryption algorithm combining two-dimensional modular hyperchaotic map and compressed sensing

Recently, to offer better ensure for image privacy security, numerous new image encryption algorithms have been proposed. However, these algorithms still suffer from the problems of chaotic performance scarcity, low encryption effect, and high consumption of computational resources. To solve the above issues, we first construct a two-dimensional modular hyperchaotic map (2D-MHM). Then, we further develop an image encryption algorithm based on 2D-MHM and compressed sensing (CS). Several chaotic metrics verify the randomness and validity of 2D-MHM. These metrics include bifurcation diagram, Lyapunov exponent, initial value sensitivity, 0–1 test, and NIST test. Specifically, CS significantly reduces the ciphertext image size thereby reducing its resource consumption during transmission. Reality-preserving fractional DCT (RP-Fdct) diffusion is utilized to transform pixels into the frequency domain to enhance the encryption effect. Subsequently, lightweight index confusion and XOR diffusion further improve the algorithm security. The security of the algorithm is verified through various experiments. It is able to encrypt grayscale and color images of different sizes with good results. Notably, this algorithm also implements the encryption requirements for binary images. Due to our designs, it outperforms recently reported encryption algorithms in several areas, especially in reconstruction performance.

AmLuCEP: Amalgamating LUT-based Compression and Adaptive Encoding Assisted Block Placement To Improve Lifetime of PCM-based Main Memories

With the rising demands for high capacity memory and poor scalability of the existing DRAM-based main memories, the emerging Non-volatile memories captures higher attention due to their high density and low leakage power consumption. However, the possible consideration of such memories as alternatives of DRAM is largely hindered by their intrinsic drawbacks like high write latency, high write energy and low write endurance. In this article, we propose an integrated solution by combining the effect of compression and encoding assisted block placement to improve lifetime of NVMs. We have developed a compression technique called LUT_Comp by exploiting the word-level redundancy present in the words of the incoming cache blocks to NVM. LUT_Comp remains effective in reducing bit-flips in NVMs by offering a balance in compression ratio and coverage (Cov). Additionally, we also propose an encoding based block placement policy that places the compressed blocks in the appropriate half within the memory. The integrated approach of compression and block placement termed AmLuCEP offers a uniform bit-flips distribution while further reducing bit-flips in NVM. Experimental results show that AmLuCEP reduces bit-flips by 54%, 42%, 37%, 21%; energy consumption by 41%, 28%, 24%, 13%; and improves lifetime by 57%, 40%, 38%, 21% over baseline and the existing techniques READ [ 38 ], COEF [ 39 ] and SELEC [ 13 ], respectively.

An efficient image compression encryption scheme based on BP neural network and DNA mutation

In this paper, an efficient image compression encryption scheme is proposed. Firstly, compressing the image by using BP neural network before encryption, which not only saves the subsequent encryption and decryption time but also minimizes storage and transmission space. Secondly, Chialvo neuron model is used to generate chaotic sequences for the encryption process in the scheme, confusing the compressed image according to a confusion algorithm combining single-plane and cross-plane. Next, diffusing the confused image. Subsequently, the diffused image and chaotic sequences are DNA encoded, performing DNA same or operation (DNA mutual mutations) and DNA mutations (DNA self-mutations). Finally, the DNA mutations results are decoded to obtain the cipher image. Security tests and compression performance analysis show that the scheme can efficiently and securely compress and encrypt images of different sizes with higher reconstruction quality compared to other compression schemes.

Image privacy protection scheme based on high-quality reconstruction DCT compression and nonlinear dynamics

The protection of digital image privacy in Big Data environment is a hot issue of increasing concern. To address the contradiction between efficiency and security for privacy protection, this paper proposes a high-quality restored image privacy protection scheme based on Discrete Cosine Transform (DCT) frequency domain compression and nonlinear dynamics. First, the RGB space of the color image is converted to YCbCr domain with 4:2:0 sampling. Then, the image is divided into sub-blocks in the spatial domain, followed by block-wise DCT and quantization into frequency domain information. Next, both Alternating Current (AC) and Direct Current (DC) coefficients of all sub-blocks are individually extracted and subjected to compression encoding. Finally, a Rubik’s Cube permutation and filtering diffusion encryption algorithm based on a nonlinear dynamical chaotic system is designed. Moreover, a mechanism for generating dynamic chaotic sequences associated with plaintext is introduced to effectively counter cryptographic attacks. The combined approach of compression and encryption significantly reduces computational complexity while improving encryption efficiency. Through experimental simulation results, the compression encryption scheme demonstrates high compression ratios as well as excellent image recovery quality while providing enhanced security against common cryptographic attacks. The work in this paper provides a preferred joint compression and encryption technical solution for digital image privacy protection in Big Data network.

Modern approaches to compression and noise resistant encoding of photo and video information in radio channels of complexes with unmanned aerial vehicle

Modern complexes with unmanned aerial vehicles (UAVs) of long flight duration (heavy class) are in practice equipped with a wide range of target loads, which leads to the need to transmit a large amount of useful information in real time with the required quality to a remote ground control station (GCS). The large volume of transmitted information imposes serious technical requirements on the capacity of communication channels, which, on the one hand, when designing communication systems within UAVs, is ensured by the use of directional antenna systems, multi-position signal-code structures, noise-resistant coding, high energy in the communication channel, etc. On the other hand, developers simultaneously solve the problem of minimizing the amount of information from target loads supplied to the input of the communication system while maintaining the required quality after compression. This article discusses the main information compression standards used by CUAVs developers, their advantages and disadvantages, as well as video codecs based on three-dimensional orthogonal transformations.

A multiple-image encryption method based on bimodal biometric keys

In this paper, a multiple-image encryption method based on bimodal biometric keys is proposed, which has the advantages of high level of security, high encryption efficiency, high security and good convenience as well as strong robustness of bimodal biometric keys. In the encryption process, each plaintext image is firstly encoded into corresponding QR code and subsequently encrypted by using a compressive encryption method with fingerprint chaotic measurement matrix of each encryption user and a filtering encryption method in discrete cosine transform domain with iris chaotic mask of each encryption user successively, then the encryption complex amplitude is generated with the help of grating modulation technology and matrix splicing technology, thereafter the encryption complex amplitude is finally encrypted into two hologram ciphertexts by using a two-step phase-shift digital holography with fingerprint chaotic phase mask of encryption administrator. In the decrytion process, the decryption administrator first passes the fingerprint authentication and then the decryption complex amplitude is generated, thereafter multiple-image decryption can be achieved through the fingerprint authentication and iris authentication of each decryption user. In order to demonstrate the feasibility of the proposed multiple-image encryption method, a series of numerical simulations are performed, and the simulation results show that the proposed method exhibits high feasibility as well as level of security and the bimodal biometric keys possess high security and strong robustness.

An image compression encryption scheme based on chaos and SPECK-DCT hybrid coding

An image compression encryption scheme based on chaos and SPECK-DCT hybrid coding

Exploiting high-quality reconstruction image encryption strategy by optimized orthogonal compressive sensing

Compressive sensing is favored because it breaks through the constraints of Nyquist sampling law in signal reconstruction. However, the security defects of joint compression encryption and the problem of low quality of reconstructed image restoration need to be solved urgently. In view of this, this paper proposes a compressive sensing image encryption scheme based on optimized orthogonal measurement matrix. Utilizing a combination of DWT and OMP, along with chaos, the proposed scheme achieves high-security image encryption and superior quality in decryption reconstruction. Firstly, the orthogonal optimization method is used to improve the chaotic measurement matrix. Combined with Part Hadamard matrix, the measurement matrix with strong orthogonal characteristics is constructed by Kronecker product. Secondly, the original image is sparsely represented by DWT. Meanwhile, Arnold scrambling is used to disturb the correlation between its adjacent pixels. Following this, the image is compressed and measured in accordance with the principles of compressive sensing and obtain the intermediate image to be encrypted. Finally, the chaotic sequence generated based on 2D-LSCM is used to perform on odd-even interleaved diffusion and row-column permutation at bit-level to obtain the final ciphertext. The experimental results show that this scheme meets the cryptographic requirements of obfuscation, diffusion and avalanche effects, and also has a large key space, which is sufficient to resist brute-force cracking attacks. Based on the sparse and reconstruction algorithm of compressive sensing proposed in this paper, it has better image restoration quality than similar algorithms. Consequently, the compressive sensing image encryption scheme enhances both security and reconstruction quality, presenting promising applications in the evolving landscape of privacy protection for network big data.

Second-Order Motion-Compensated Echo-Planar Cardiac Diffusion-Weighted MRI: Usefulness of Compressed Sensitivity Encoding.

Cardiac diffusion-weighted imaging (DWI) using second-order motion-compensated spin echo (M2C) can provide noninvasive in-vivo microstructural assessment, but limited by relatively low signal-to-noise ratio (SNR). Echo-planar imaging (EPI) with compressed sensitivity encoding (EPICS) could address these issues. To combine M2C DWI and EPCIS (M2C EPICS DWI), and compare image quality for M2C DWI. Prospective. Ten ex-vivo hearts, 10 healthy volunteers (females, 5 [50%]; mean ± SD of age, 25 ± 4 years), and 12 patients with diseased hearts (female, 1 [8.3%]; mean ± SD of age, 44 ± 16 years; including coronary artery heart disease, congenital heart disease, dilated cardiomyopathy, amyloidosis, and myocarditis). 3-T, M2C EPICS DWI, and M2C DWI. The apparent SNR (aSNR) and the rating scores were used to evaluate and compared image quality of all three groups. The aSNR was calculated using , and the myocardium was segmented manually. Three observers independently rated subjective image quality using a 5-point Likert scale. Bland-Altman analysis and paired t-tests. The threshold for statistical significance was set at P < 0.05. In healthy volunteers, the aSNR with a b-value of 450 s/mm2 acquired by M2C EPICS DWI was significantly higher than M2C DWI at in-plane resolutions of 3.0 × 3.0, 2.5 × 2.5, and 2.0 × 2.0 mm2. In patients with diseased hearts, the aSNR ofM2C EPICS DWI was also significantly higher than that for M2C DWI (bias of M2C EPICS-M2C = 1.999, 95% limits of agreement, 0.362 to 3.636; mean ± SD, 7.80 ± 1.37 vs. 5.80 ± 0.81). The ADC values of M2C EPICS was significantly higher than M2C DWI in in-vivo hearts. Over 80% of the images with rating scores for M2C EPICS DWI were higher than M2C DWI in in-vivo hearts. Cardiac imaging by M2C EPICS DWI may demonstrate better overall image quality and higher aSNR than M2C DWI. 2 TECHNICAL EFFICACY: Stage 1.

Joint watermarking encryption compression algorithm for securing an e-healthcare application

Joint watermarking encryption compression algorithm for securing an e-healthcare application

LSB-2 Steganography with Brotli Compression and base64 Encoding for Improving Data Embedding Capacity

Steganography functions as a technique for embedding messages or data in various forms of media, such as images, audio, video, or text, with the aim of avoiding detection by unauthorized parties. Steganography techniques can be used as a solution to hide and protect data. In this research, steganography will be carried out using images as the transmission object. This research was conducted to offer a modification of the Least Significant Bit (LSB) steganography technique using the LSB-2 method with Brotli compression and base64 encoding. Modification and use of Brotli compression and base64 coding aims to increase the message capacity that can be embedded in a transmission object while maintaining the quality of the transmission object. Experiments using small data and big data. The experimental results will be presented in tabular form by comparing the original image with the steganographically processed image using metrics such as Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM) as a comparison. The experiments carried out resulted in an increase in image capacity by reducing capacity usage with an average of 47.13% for small data and an average of 71.34%. The big data experiment resulted in an increase in the PSNR value of around 3.49%, accompanied by a decrease in the average MSE value of 33.85%, and a constant SSIM value of 0.9999, thus proving that the proposed method was successful in increasing image capacity and improving stego-image quality. when embedding big data.

On the Robustness of Neural-Enhanced Video Streaming against Adversarial Attacks

The explosive growth of video traffic on today's Internet promotes the rise of Neural-enhanced Video Streaming (NeVS), which effectively improves the rate-distortion trade-off by employing a cheap neural super-resolution model for quality enhancement on the receiver side. Missing by existing work, we reveal that the NeVS pipeline may suffer from a practical threat, where the crucial codec component (i.e., encoder for compression and decoder for restoration) can trigger adversarial attacks in a man-in-the-middle manner to significantly destroy video recovery performance and finally incurs the malfunction of downstream video perception tasks. In this paper, we are the first attempt to inspect the vulnerability of NeVS and discover a novel adversarial attack, called codec hijacking, where the injected invisible perturbation conspires with the malicious encoding matrix by reorganizing the spatial-temporal bit allocation within the bitstream size budget. Such a zero-day vulnerability makes our attack hard to defend because there is no visual distortion on the recovered videos until the attack happens. More seriously, this attack can be extended to diverse enhancement models, thus exposing a wide range of video perception tasks under threat. Evaluation based on state-of-the-art video codec benchmark illustrates that our attack significantly degrades the recovery performance of NeVS over previous attack methods. The damaged video quality finally leads to obvious malfunction of downstream tasks with over 75% success rate. We hope to arouse public attention on codec hijacking and its defence.

Low-cost ANS encoder for lossless data compression in FPGAs

We present the implementation of the hardware ANS compressor in FPGAs. The main goal of the design was to propose a solution suitable to low-cost, low-energy embedded systems. We propose the streaming-rANS algorithm of the ANS family as a target for the implementation. Also, we propose a set of algorithm parameters that substantially reduce the use of FPGA resources, and we examine what is the influence of the chosen parameters on compression performance. Further, we compare our design to the lossless codecs found in literature, and to the streaming-rANS codecs with arbitrary parameters.

Fast imaging of lenticulostriate arteries by high-resolution black-blood T1-weighted imaging with variable flip angles and acceleration by compressed sensitivity encoding

ObjectiveWe investigated the feasibility of using compressed sensitivity encoding (CS-SENSE) to accelerate high-resolution black-blood T1-weighted imaging with variable flip angles (T1WI-VFA) for efficient visualization and characterization of lenticulostriate arteries (LSAs) on a 3.0 T MR scanner. Materials and methodsTwenty-five healthy volunteers and 18 patients with the cerebrovascular disease were prospectively enrolled. Healthy volunteers underwent T1WI-VFA sequences with different acceleration factors (AFs), including conventional sensitivity encoding (SENSE) AF = 3 and CS-SENSE AF = 3, 4, 5, and 6 (SENSE3, CS3, CS4, CS5, CS6, respectively) at 3 Tesla MRI scanner. Objective evaluation (contrast ratio and number, length, and branches of LSAs) and subjective evaluation (overall image quality and LSA visualization scores) were used to assess image quality and LSA visualization. Comparisons were performed among the 5 sequences to select the best AF. All patients underwent both T1WI-VFA with the optimal AF and digital subtraction angiography (DSA) examination, and the number of LSAs observed by T1WI-VFA was compared with that by DSA. ResultsPair-wise comparisons among CS3, CS4, and SENSE3 revealed no significant differences in both objective measurements and subjective evaluation (all P > 0.05). In patients, there was no significant difference in LSA counts on the same side between T1WI-VFA with CS4 and DSA (3, 3–4 and 3, 3–3, P = 0.243). ConclusionsCS3 provided better LSA visualization but a longer scan duration compared to CS4. And, CS4 strikes a good balance between LSA visualization and acquisition time, which is recommended for routine clinical use.

Empirical Evaluation of Coding and Inter Pixel Redundancy in still Image Compression

The main aim of this research work is to compress grayscale images efficiently using prediction and intensity-based image compression algorithms. Image compression is useful for removing the duplication in an image to store and transmit the data in an efficient form. This research work analyzes four new schemes for gray scale lossy image compression. Among the four schemes considered, two compressive approaches are designed for Prediction Based Image Compression (PBIC) level implementation. Third approach is designed for Intensity Based Image Compression (IBIC). Finally, the previously designed PBIC and IBIC schemes lead to an Integrated Encoder. All the considered method performances are analyzed using the performance metrics. These results are compared with JPEG 2000 which is a extensively used benchmark compression encoder. The outcome of all the proposed methods is also compared with modern encoders.

Improving Vessel Visibility and Applying Artificial Intelligence to Autodetect Brain Metastasis for a 3D MR Imaging Sequence Capable of Simultaneous Images with and without Blood Vessel Suppression.

The purposes of this study were 1) to improve vessel visibility of our MR sequence by modifying k-space filling and 2) to verify the usefulness of applying artificial intelligence (AI) for volume isotropic simultaneous interleaved bright- and black-blood examination (VISIBLE) with compressed sensitivity encoding (CS) in autodetecting brain metastases. We modified 3 sequences of VISIBLE (Centric, Reversed Centric, and Startup Echo 30). The Centric sequence is a prototype. The Reversed Centric filled the k-space in a reversed centric manner to improve vessel visibility. The Startup Echo 30 implemented dummy echoes to further improve vessel visibility. Vessel visibility was evaluated in one slice at the level of the centrum semiovale. The sensitivity, specificity, the area under the curve (AUC), and false positives of detecting brain metastases using AI were evaluated among 3 sequences. Statistical comparisons were performed using a one-way analysis of variance, followed by Friedman and Dunn's multiple comparison tests. The number of visualized vessels was significantly lower in the Centric (39.3 ± 9.7, P < 0.05) and Reversed Centric (44.2 ± 9.8, P < 0.05) methods than in the magnetization-prepared rapid gradient echo (49.3 ± 9.1) but comparable in the Startup Echo 30 method (44.9 ± 8.8, P > 0.05). No significant differences existed in sensitivity, specificity, and AUC among the 3 methods. False positives achieved using the Reversed Centric method were significantly fewer (54 false positives) than those achieved using the Centric (85 false positives) and Startup Echo 30 (68 false positives) methods (P = 0.0092). Vessel visibility was improved by modifying the k-space filling, which may reduce false positives. The AI model for VISIBLE with CS achieved good performance in autodetection of brain metastases. The AI model for VISIBLE with CS can help radiologists diagnose brain metastases in clinical practice.

Evaluation of the Latest Motion Correction Techniques in Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction (PROPELLER) Imaging across Different Vendors

To evaluate the robustness of the latest periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) technology from each vendor against head movements and to investigate their characteristics for effective clinical use. Using a phantom simulating the T2-weighted image of the human brain, images were acquired with devices from CANON MEDICAL SYSTEMS (Tochigi, Japan; hereinafter "Canon"), GE HealthCare (Chicago, IL, USA; hereinafter "GE"), Philips (Amsterdam, Netherlands), and Siemens Healthineers (Forchheim, Germany; hereinafter "SIEMENS"). The head motion patterns were divided into rotation angle dependency (single rotation and multiple rotations) and rotation frequency dependency and evaluated using structural similarity (SSIM). For rotation angle dependency, Canon was robust against small rotation angles and fine movements. Despite the rotation angle, GE was robust against movements, with deep learning reconstruction (DLR) improving correction functionality. Philips could be used with compressed sensitivity encoding (CS), and robustness varied with blade width. SIEMENS was robust against large movements. For rotation frequency dependency, results were similar across the 4 vendors. The rotation angle and rotation frequency dependencies of the PROPELLER technology from the 4 vendors were quantitatively evaluated. Understanding the characteristics of PROPELLER allows for the possibility of providing diagnostic-quality images even for patients who move during head MRI exams by appropriately using PROPELLER.