Masking Algorithm Research Articles

An Image Quality Evaluation and Masking Algorithm Based On Pretrained Deep Neural Networks

With the growing amount of astronomical data, there is an increasing need for automated data processing pipelines, which can extract scientific information from observation data without human interventions. A critical aspect of these pipelines is the image quality evaluation and masking algorithm, which evaluate image qualities based on various factors such as cloud coverage, sky brightness, scattering light from the optical system, point-spread-function size and shape, and read-out noise. Occasionally, the algorithm requires masking of areas severely affected by noise. However, the algorithm often necessitates significant human interventions, reducing data processing efficiency. In this study, we present a deep-learning-based image quality evaluation algorithm that uses an autoencoder to learn features of high quality astronomical images. The trained autoencoder enables automatic evaluation of image quality and masking of noise affected areas. We have evaluated the performance of our algorithm using two test cases: images with point spread functions of varying full width half magnitude, and images with complex backgrounds. In the first scenario, our algorithm could effectively identify variations of the point spread functions, which can provide valuable reference information for photometry. In the second scenario, our method could successfully mask regions affected by complex regions, which could significantly increase the photometry accuracy. Our algorithm can be employed to automatically evaluate image quality obtained by different sky surveying projects, further increasing the speed and robustness of data processing pipelines.

A chirplet‐based masking algorithm for smeared spectrum jamming suppression and signal separation

AbstractLinear frequency modulation (LFM) signal is a common radar signal in modern electronic warfare, and smeared spectrum (SMSP) can generate multiple false targets, causing jamming to radar detection. The authors propose a chirplet‐based masking algorithm that can solve the problem of SMSP jamming suppression and address a more complex problem: the separation of jamming signal and multiple LFM signals from intercepted mixed signal. First, the authors obtain matched chirp rates of the source signals through the changing tendency of the Rényi entropy. Then, the ridge of each source signal is extracted from the high‐resolution chirplet transform result using an image processing‐based algorithm. Finally, the jamming and LFM signals are accurately reconstructed through the time‐frequency mask to achieve separation. Even in the extreme case where multiple source signals with close chirp rates are overlapped, the proposed slope‐matching ridge extraction method and iterative update reconstruction method can still achieve commendable signal separation effects. Extensive experimental results demonstrate that the proposed algorithm performs well under extreme conditions of low signal‐to‐noise ratio, high jamming‐to‐signal ratio, and high sea state.

U-Plume: automated algorithm for plume detection and source quantification by satellite point-source imagers

Abstract. Current methods for detecting atmospheric plumes and inferring point-source rates from high-resolution satellite imagery are labor-intensive and not scalable with regard to the growing satellite dataset available for methane point sources. Here, we present a two-step algorithm called U-Plume for automated detection and quantification of point sources from satellite imagery. The first step delivers plume detection and delineation (masking) with a U-Net machine learning architecture for image segmentation. The second step quantifies the point-source rate from the masked plume using wind speed information and either a convolutional neural network (CNN) or a physics-based integrated mass enhancement (IME) method. The algorithm can process 62 images (each measuring 128 pixels × 128 pixels) per second on a single 2.6 GHz Intel Core i7-9750H CPU. We train the algorithm using large-eddy simulations of methane plumes superimposed on noisy and variable methane background scenes from the GHGSat-C1 satellite instrument. We introduce the concept of point-source observability, Ops=Q/(UWΔB), as a single dimensionless number to predict plume detectability and source rate quantification error from an instrument as a function of source rate Q, wind speed U, instrument pixel size W, and instrument-dependent background noise ΔB. We show that Ops can powerfully diagnose the ability of an imaging instrument to observe point sources of a certain magnitude under given conditions. U-Plume successfully detects and masks plumes from sources as small as 100 kg h−1 in GHGSat-C1 images over surfaces with low background noise and successfully handles larger point sources over surfaces with substantial background noise. We find that the IME method for source quantification is unbiased over the full range of source rates, while the CNN method is biased towards the mean of its training range. The total error in source rate quantification is dominated by wind speed at low wind speeds and by the masking algorithm at high wind speeds. A wind speed of 2–4 m s−1 is optimal for detection and quantification of point sources from satellite data.

A provably masked implementation of BIKE Key Encapsulation Mechanism

BIKE is a post-quantum key encapsulation mechanism (KEM) selected for the 4th round of the NIST's standardization campaign. It relies on the hardness of the syndrome decoding problem for quasi-cyclic codes and on the indistinguishability of the public key from a random element, and provides the most competitive performance among round 4 candidates, which makes it relevant for future real-world use cases. Analyzing its side-channel resistance has been highly encouraged by the community and several works have already outlined various side-channel weaknesses and proposed ad-hoc countermeasures. However, in contrast to the well-documented research line on masking lattice-based algorithms, the possibility of generically protecting code-based algorithms by masking has only been marginally studied in a 2016 paper by Chen et al. in SAC 2015. At this stage of the standardization campaign, it is important to assess the possibility of fully masking BIKE scheme and the resulting cost in terms of performances. In this work, we provide the first high-order masked implementation of a code-based algorithm. We had to tackle many issues such as finding proper ways to handle large sparse polynomials, masking the key-generation algorithm or keeping the benefit of the bitslicing. In this paper, we present all the gadgets necessary to provide a fully masked implementation of BIKE, we discuss our different implementation choices and we propose a full proof of masking in the Ishai Sahai and Wagner (Crypto 2003) model. More practically, we also provide an open C-code masked implementation of the key-generation, encapsulation and decapsulation algorithms with extensive benchmarks. While the obtained performance is slower than existing masked lattice-based algorithms, we show that masking at order 1, 2, 3, 4 and 5 implies a performance penalty of x5.8, x14.2, x24.4, x38 and x55.6 compared to order 0 (unmasked and unoptimized BIKE). This scaling is encouraging and no Boolean to Arithmetic conversion has been used.

An Edge Detection Algorithm for SEM Images of Multilayer Thin Films

In processing multilayer thin film materials, scanning electron microscopy (SEM) is commonly employed for observation. In images of SEM, backscattered electron (BSE) images is particularly suitable for distinguishing different components and layers of the films. However, at high magnification levels, BSE images often have blurriness and noise, leading to low edge sharpness. This study proposes a method for improving the integrity and accuracy of the edges. First, we segment the image into different contrast regions using the masking algorithm. Second, we enhance the images in separate regions by the enhancement algorithm. Finally, we combine the regions by logical operations. In instantiation, we implement our approach on SEM-BSE images. It was found that the edges are significantly sharpened through the assessment of the edge evaluation algorithm.

TIM‐Net: A multi‐label classification network for TCM tongue images fusing global‐local features

AbstractCombining the extracted tongue features with other medical indicators can effectively judge the diseases of patients. The previous work usually only analyzes a certain feature of the tongue body and is unable to extract multiple features simultaneously. In this study, a multi‐label classification network named TIM‐Net is proposed, which integrates global and local features to achieve multi‐label intelligent diagnosis of Chinese medicine tongue images. First, a feature extraction network based on ResNet is proposed to capture the features of tongue images more sufficiently. Then, a multi‐label classification algorithm fusing global and local features is proposed, and targeted screening operations are carried out on the class‐related feature maps based on global confidence. In addition, a logical masking algorithm is proposed to ensure that the local features can only correct the feature labels they represent, and do not interfere with other feature labels. The classification accuracy is further improved by using local feature confidence and correcting the global classification results. Finally, the experimental results indicate that the classification accuracy of the tongue images is gradually improved through optimizing the feature extraction network and fusing local features, and it exceeds other state‐of‐the‐art multi‐label classification networks.

Causal relationship recognition algorithm based on external semantic and contextual structural features

Identifying the causal relationship of events plays an important role in determining the development of known events and evaluating the possible outcomes of different decisions. At present, neural network models are widely used to identify the relationships between events. Based on obtaining events, researchers distinguish the relationships between events by mining their semantics. However, due to the complexity of events and the dynamic changes in relationships between events, models often cannot fully meet the needs of accurately identifying causal relationships by only learning simple event descriptions in sentences; Moreover, focusing too much on the events themselves often leads to neglecting the structural features of statements and neglecting the impact of specific structural patterns on the relationships between events. In this article, we propose an event masking algorithm that combines external semantics to address the aforementioned issues. In this algorithm, external semantics are first introduced into the statement to enrich the information behind the event, allowing the model to mine the deep connections between events through a wider range of background knowledge; Then, the event masking module is used to enhance the model's extraction of sentence structured features, mining specific contextual representations that are unrelated to the event. The results show that compared to existing neural network algorithms, the algorithm proposed in this paper improves the F1 value of predictions on publicly available datasets by more than 4%.

Early Diagnosing and Transformation Prediction of Alzheimer's Disease Using Multi-Scaled Self-Attention Network on Structural MRI Images with Occlusion Sensitivity Analysis.

Structural magnetic resonance imaging (sMRI) is vital for early Alzheimer's disease (AD) diagnosis, though confirming specific biomarkers remains challenging. Our proposed Multi-Scale Self-Attention Network (MUSAN) enhances classification of cognitively normal (CN) and AD individuals, distinguishing stable (sMCI) from progressive mild cognitive impairment (pMCI). This study leverages AD structural atrophy properties to achieve precise AD classification, combining different scales of brain region features. The ultimate goal is an interpretable algorithm for this method. The MUSAN takes whole-brain sMRI as input, enabling automatic extraction of brain region features and modeling of correlations between different scales of brain regions, and achieves personalized disease interpretation of brain regions. Furthermore, we also employed an occlusion sensitivity algorithm to localize and visualize brain regions sensitive to disease. Our method is applied to ADNI-1, ADNI-2, and ADNI-3, and achieves high performance on the classification of CN from AD with accuracy (0.93), specificity (0.82), sensitivity (0.96), and area under curve (AUC) (0.95), as well as notable performance on the distinguish of sMCI from pMCI with accuracy (0.85), specificity (0.84), sensitivity (0.74), and AUC (0.86). Our sensitivity masking algorithm identified key regions in distinguishing CN from AD: hippocampus, amygdala, and vermis. Moreover, cingulum, pallidum, and inferior frontal gyrus are crucial for sMCI and pMCI discrimination. These discoveries align with existing literature, confirming the dependability of our model in AD research. Our method provides an effective AD diagnostic and conversion prediction method. The occlusion sensitivity algorithm enhances deep learning interpretability, bolstering AD research reliability.

Large Range of a High-Precision, Independent, Sub-Mirror Three-Dimensional Co-Phase Error Sensing and Correction Method via a Mask and Population Algorithm.

The emergence of segmented mirrors is expected to solve the design, processing, manufacturing, testing, and launching of space telescopes of large apertures. However, with the increase in the number of sub-mirrors, the sensing and correction of co-phase errors in segmented mirrors will be very difficult. In this paper, an independent three-dimensional method for sub-mirror co-phase error sensing and correction method is proposed. The method is based on a wide spectral modulation transfer function (MTF), mask, population optimization algorithm, and online model-free correction. In this method, the sensing and correction process of each sub-mirror co-phase error is independent of each other, so the increase in the number of sub-mirrors will not increase the difficulty of the method. This method can sense and correct the co-phase errors of three dimensions of the sub-mirror, including piston, tip, and tilt, even without modeling the optical system, and has a wide detection range and high precision. And the efficiency is high because the sub-mirrors can be corrected simultaneously in parallel. Simulation results show that the proposed method can effectively sense and correct the co-phase errors of the sub-mirrors in the range [-50λ, 50λ] in three dimensions with high precision. The average RMSE value in 100 experiments of the true co-phase error values and the experimental co-phase error values of one of the six sub-mirrors is 2.358 × 10-7λ.

Internet Traffic Privacy Enhancement with Masking: Optimization and Trade-Offs

An increasing number of recent experimental works have demonstrated that the supposedly secure channels in the Internet are prone to privacy breaking under many respects, due to packet traffic features leaking information on the user activity and traffic content. We aim at understanding if and how complex it is to obfuscate the information leaked by packet traffic features, namely packet lengths, directions, times: we call this technique traffic masking. We define a security model that points out what the ideal target of masking is, and then define the optimized traffic masking algorithm that removes any leaking (full masking). Further, we investigate the trade-off between traffic privacy protection and masking cost, namely required amount of overhead and realization complexity/feasibility. Numerical results are based on measured Internet traffic traces. Major findings are that: i) optimized full masking achieves similar overhead values with padding only and in case fragmentation is allowed; ii) if practical realizability is accounted for, optimized statistical masking attains only moderately better overhead than simple fixed pattern masking does, while still leaking correlation information that can be exploited by the adversary.

Binocular Vision-Based Non-Singular Fast Terminal Control for the UVMS Small Target Grasp

Autonomous underwater manipulation is very important for the robotic and intelligence operations of oceanic engineering. However, a small target often involves limited features and results in inaccurate visual matching. In order to improve visual measurement accuracy, this paper has proposed an improved unsharp masking algorithm to further enhance the weak texture region of blurred and low contrast images. Moreover, an improved ORB feature-matching method with adaptive threshold, non-maximum suppression and improved random sample consensus has also been proposed. To overcome unknown underwater disturbances and uncertain system parameters in the underwater robotic manipulations, an adaptive non-singular terminal sliding mode controller has been proposed with a quasi-barrier function to suppress the chattering problem and improve grasp accuracy for small target. Oceanic experiments have been conducted to prove the performance of the proposed method.

Chinese Speech Enhancement and Adaptive Recognition Technology for Complex Language Environments

The development of intelligent technology has also made rapid progress in relevant speech fields. In order to increase the application scenarios of speech recognition systems, the research has improved the traditional Speech enhancement algorithm, namely the Ideal Binary Mask (IBM) algorithm, and combined it with the unimproved IBM algorithm to propose an adaptive IBM algorithm. Based on this algorithm, the research has built a new speech recognition system, The system uses an FIR filter to realize pre-emphasis processing and uses Berouti spectral subtraction to preprocess speech. The Speech enhancement model is built using a deep learning network model. The results showed that the IBM algorithm had the highest score in the Perceptual Evaluation of Speech Quality (PESQ) at 3.5596, followed by the Ideal Ratio Mask (IRM) algorithm at 3.3429. The improvement of the IBM algorithm was feasible when the noise intensity coefficient was greater than 0.008. When the noise intensity coefficient was greater than 0.08, the average score of the improved IBM algorithm was 2.1079, and the average score of the unimproved IBM algorithm was 1.9418. The proposed adaptive IBM algorithm has higher performance in complex speech environments compared to the original system.

Gradient-adaptive Nonlinear Sharpening for Dental Radiographs

Unsharp Masking is a popular image processing technique used for improving the sharpness of structures on dental radiographs. However, it produces overshoot artefact and intolerably amplifies noise. On radiographs, the overshoot artefact often resembles the indications of prosthesis misfit, pathosis, and pathological features associated with restorations. A noise- robust alternative to the Unsharp Masking algorithm, termed Gradient-adaptive Nonlinear Sharpening (GNS) which is free from overshoot and discontinuity artefacts, is proposed in this paper. In GNS, the product of the arbitrary scalar termed as ‘scale’ and the difference between the output of the Adaptive Edge Smoothing Filter (AESF) and the input image, weighted by the normalized gradient magnitude is added to the input image. AESF is a locally-adaptive 2D Gaussian smoothing kernel whose variance is directly proportional to the local value of the gradient magnitude. The dataset employed in this paper is downloaded from the Mendeley data repository having annotated panoramic dental radiographs of 116 patients. On 116 dental radiographs, the values of Saturation Evaluation Index (SEI), Sharpness of Ridges (SOR), Edge Model Based Contrast Metric (EMBCM), and Visual Information Fidelity (VIF) exhibited by the Unsharp Masking are 0.0048 ± 0.0021, 4.4 × 1013 ± 3.8 × 1013, 0.2634 ± 0.2732 and 0.9898 ± 0.0122. The values of these quality metrics corresponding to the GNS are 0.0042 ± 0.0017, 2.2 × 1013 ± 1.8 × 1013, 0.5224 ± 0.1825, and 1.0094 ± 0.0094. GNS exhibited lower values of SEI and SOR and higher values of EMBCM and VIF, compared to the Unsharp Masking. Lower values of SEI and SOR, respectively indicate that GNS is free from overshoot artefact and saturation and the quality of edges in the output images of GNS is less affected by noise. Higher values of EMBCM and VIF, respectively confirm that GNS is free from haloes as it produces thin and sharp edges and the sharpened images are of good information fidelity.

Fast but approximate homomorphic k-means based on masking technique

Nowadays, computing on encrypted data seems to be more practical than a few years ago, thanks to the emergence of new Homomorphic Encryption schemes. In this paper, an algorithm based on Homomorphic Encryption for Arithmetic of Approximate Numbers (Cheon et al., in: Takagi, Peyrin (eds) Advances in cryptology—ASIACRYPT 2017, Springer, Cham, pp 409–437, 2017) (HEAAN, or also CKKS) scheme, that is able to perform a secure k-means algorithm which processes encrypted data, has been studied and presented. The performance of the classifier running on encrypted data has been evaluated using a standard k-means algorithm that works on plain data as a supervised structure, since the results are obtained by approximated computations. The main point of this paper is to take existent theoretical techniques (for example approximations of text {sgn}(x)), to use them and to observe if they are valid in practical applications. The output of the algorithm is a set of k encrypted masks that can be applied to the original dataset in order to obtain different clusters. The setting is a standard client–server one. The workload is heavily server-centric, as the client only has to execute a light masking algorithm at the end of each iteration, which, excluding the decryption, is faster than a plain k-means iteration; the main disadvantage concerns the accuracy of the results. Experiments show that the algorithm can be executed fairly quickly: the execution time of the training phase is on the order of seconds, while classification is on the order of tenths of a second.

Assessing the Effectiveness of Masking and Encryption in Safeguarding the Identity of Social Media Publishers from Advanced Metadata Analysis

Machine learning algorithms, such as KNN, SVM, MLP, RF, and MLR, are used to extract valuable information from shared digital data on social media platforms through their APIs in an effort to identify anonymous publishers or online users. This can leave these anonymous publishers vulnerable to privacy-related attacks, as identifying information can be revealed. Twitter is an example of such a platform where identifying anonymous users/publishers is made possible by using machine learning techniques. To provide these anonymous users with stronger protection, we have examined the effectiveness of these techniques when critical fields in the metadata are masked or encrypted using tweets (text and images) from Twitter. Our results show that SVM achieved the highest accuracy rate of 95.81% without using data masking or encryption, while SVM achieved the highest identity recognition rate of 50.24% when using data masking and AES encryption algorithm. This indicates that data masking and encryption of metadata of tweets (text and images) can provide promising protection for the anonymity of users’ identities.

MaskSIMD-lib: on the performance gap of a generic C optimized assembly and wide vector extensions for masked software with an Ascon-p test case

Efficient implementations of software masked designs constitute both an important goal and a significant challenge to Side Channel Analysis attack (SCA) security. In this paper we discuss the shortfall between generic C implementations and optimized (inline-) assembly versions while providing a large spectrum of efficient and generic masked implementations for any order, and demonstrate cryptographic algorithms and masking gadgets with reference to the state of the art. Our main goal is to show the prime performance gaps we can expect between different implementations and suggest how to harness the underlying hardware efficiently, a daunting task for various masking-orders or masking algorithm (multiplications, refreshing etc.). This paper focuses on implementations targeting wide vector bitsliced designs, such as the ISAP algorithm. We explore concrete instances of implementations utilizing processors enabled by wide-vector capability extensions of the AMD64 Instruction Set Architecture (ISA); namely, the SSE2/3/4.1, AVX-2 and AVX-512 Streaming Single Instruction Multiple Data extensions. These extensions mainly enable efficient memory level parallelism and provide a gradual reduction in computation-time as a function of the level of extension and the hardware support for instruction-level parallelism. For the first time we provide a complete open-source repository of such gadgets tailored for these extensions, various gadgets types and for all orders. We evaluate the disparities between generic high-level language masking implementations for optimized (inline-) assembly and conventional single execution path data-path architectures such as the ARM architecture. We underscore the crucial trade-off between state storage in the data-memory as compared to keeping it in the register-file (RF). This relates specifically to masked designs, and is particularly difficult to resolve because it requires inline-assembly manipulations and is not natively supported by compilers. Moreover, as the masking order (d) increases and the state gets larger, there must be an increase in data memory read/write accesses for state handling since the RF is simply not large enough. This requires careful optimization which depends to a considerable extent on the underlying algorithm to implement. We discuss how full utilization of SSE extensions is not always possible; i.e. when d is not a power of two, and pin-point the optimal d values and very sub-optimal values of d which aggressively under-utilize the hardware. More generally, this paper presents several different fully generic masked implementations for any order or multiple highly optimized (inline-) assembly instances which are quite generic (for a wide spectrum of ISAs and extensions), and provide very specific implementations targeting specific extensions. The goal is to promote open-source availability, research, improvement and implementations relating to SCA security and masked designs. The building blocks and methodologies provided here are portable and can be easily adapted to other algorithms.

Co-Visual Pattern-Augmented Generative Transformer Learning for Automobile Geo-Localization

Geolocation is a fundamental component of route planning and navigation for unmanned vehicles, but GNSS-based geolocation fails under denial-of-service conditions. Cross-view geo-localization (CVGL), which aims to estimate the geographic location of the ground-level camera by matching against enormous geo-tagged aerial (e.g., satellite) images, has received a lot of attention but remains extremely challenging due to the drastic appearance differences across aerial–ground views. In existing methods, global representations of different views are extracted primarily using Siamese-like architectures, but their interactive benefits are seldom taken into account. In this paper, we present a novel approach using cross-view knowledge generative techniques in combination with transformers, namely mutual generative transformer learning (MGTL), for CVGL. Specifically, by taking the initial representations produced by the backbone network, MGTL develops two separate generative sub-modules—one for aerial-aware knowledge generation from ground-view semantics and vice versa—and fully exploits the entirely mutual benefits through the attention mechanism. Moreover, to better capture the co-visual relationships between aerial and ground views, we introduce a cascaded attention masking algorithm to further boost accuracy. Extensive experiments on challenging public benchmarks, i.e., CVACT and CVUSA, demonstrate the effectiveness of the proposed method, which sets new records compared with the existing state-of-the-art models. Our code will be available upon acceptance.

Research on group distribution leakage analysis method for RSM

Side channel attack is a new and simple attack idea, and its proposal has created a significant threat to the security of cryptographic devices. With the continuous development of deep learning techniques, deep learning-based side channel attacks have enabled the attack techniques to reach new heights, but the attack efficiency and training time have become new problems. In order to improve the attack efficiency, researchers have proposed a large number of leakage analysis methods, which can effectively extract the intervals with significant leakage in the energy consumption curve and reduce the training parameters and data volume. In order to deal with side channel attacks, researchers have proposed various resistance strategies, among which masking protection is the simplest and widely used way. RSM rotating S-box masking scheme is a particularly important masking protection scheme, which uses a first-order masking scheme to randomize the leakage energy consumption of sensitive information using random masks, thus making the algorithm resistant to first-order side channel attacks. For this type of masking algorithm, this paper proposes a new leakage analysis algorithm, namely "Group Distribution Difference" (GDD). This algorithm is based on the distribution difference of energy consumption, and the energy consumption frequency in the group is calculated instead of the probability distribution. The KL distance is used to calculate the difference between groups and find out the leakage interval, so as to achieve an efficient attack.

Citrus fruit content and disease detection using adaptive contrast enhancement

With the advent of modernized materials and automation-based methods almost each and every field is progressing towards application of software based methods. In this article an approach using modernized software method based on content and material analysis for detection of diseases in citrus fruits is proposed. The major focus for discussion is design of pre-processing stage which extract the details present on the surface of the fruit defining the material level details. This will ensure the improvement in quality of images so as to make the whole process efficient. The algorithm proposed through this paper combines two methodologies Un-sharp masking and Genetic algorithm which aims toward improvising the contrast thereby resulting in image enhancement, food production, agricultural productivity and food gap. The proposed algorithm applied on surface level details is tested for different types of citrus diseases. Experimental results obtained through simulation on different diseased images using peak signal-to-noise ratio, structural similarity index metric, Blind Image Spatial Quality Evaluator and show that the proposed algorithm outperforms other methods for quality enhancement of images both subjectively and objectively.

Revisiting Higher-Order Masked Comparison for Lattice-Based Cryptography: Algorithms and Bit-Sliced Implementations

Masked comparison is one of the most expensive operations in side-channel secure implementations of lattice-based post-quantum cryptography, especially for higher masking orders. First, we introduce two new masked comparison algorithms, which improve the arithmetic comparison of D’Anvers et al. (2021) and the hybrid comparison method of Coron et al. (2021) respectively. We then look into implementation-specific optimizations, and show that small specific adaptations can have a significant impact on the overall performance. Finally, we implement various state-of-the-art comparison algorithms and benchmark them on the same platform (ARM-Cortex M4) to allow a fair comparison between them. We improve on the arithmetic comparison of D’Anvers et al. with a factor <inline-formula><tex-math notation="LaTeX">$\approx 20\%$</tex-math></inline-formula> by using Galois Field multiplications and the hybrid comparison of Coron et al. with a factor <inline-formula><tex-math notation="LaTeX">$\approx 25\%$</tex-math></inline-formula> by streamlining the design. Our implementation-specific improvements allow a speedup of a straightforward comparison implementation of <inline-formula><tex-math notation="LaTeX">$\approx 33\%$</tex-math></inline-formula> . We discuss the differences between the various algorithms and provide the implementations and a testing framework to ease future research.