Reviewerly/exHarmony huggingface dataset:
Improved Power Analysis Attacks on Falcon

Falcon is one of the three post-quantum signature schemes selected for standardization by NIST. Due to its low bandwidth and high efficiency, Falcon is seen as an attractive option for quantum-safe embedded systems. In this work, we study Falcon's side-channel resistance by analysing its Gaussian samplers. Our results are mainly twofold. The first result is an improved key recovery exploiting the leakage within the base sampler investigated by Guerreau et al. (CHES 2022). Instead of resorting to the fourth moment as in former parallelepiped-learning attacks, we work with the second order statistics covariance and use its spectral decomposition to recover the secret information. Our approach substantially reduces the requirement for measurements and computation resources: $$220\,000$$ traces is sufficient to recover the secret key of Falcon-512 within half an hour with a probability of $$\approx 25\%$$ . As a comparison, even with $$10^6$$ traces, the former attack still needs about 1000 h hours CPU time of lattice reduction for a full key recovery. In addition, our approach is robust to inaccurate leakage classification, which is another advantage over parallelepiped-learning attacks. Our second result is a practical power analysis targeting the integer Gaussian sampler of Falcon. The analysis relies on the leakage of random sign flip within the integer Gaussian sampling. This leakage was exposed in 2018 by Kim and Hong, but it is not considered in Falcon's implementation and unexploited for side-channel analysis until now. We identify the leakage within the reference implementation of Falcon on an ARM Cortex-M4 STM32F407IGT6 microprocessor. We also show that this single bit of leakage is in effect enough for practical key recovery: with $$170\,000$$ traces one can fully recover the key of Falcon-512 within half an hour. Furthermore, combining the sign leakage and the aforementioned leakage, one can recover the key with only $$45\,000$$ signature measurements in a short time. As a by-product, we also extend our power analysis to Mitaka which is a recent variant of Falcon. The same leakages exist within the integer Gaussian samplers of Mitaka, and they can also be used to mount key recovery attacks. Nevertheless, the key recovery in Mitaka requires much more traces than it does in Falcon, due to their different lattice Gaussian samplers.
Image Denoising Using DT-CWT Combined with ANN and Grey Wolf Optimization Algorithm

While keeping all the significant signal features as much as possible, the removal of unnecessary noise from the images is the first goal of de-noising digital images. To eliminate the noise from digital images, different methods are used. Choosing the appropriate technique for the denoising process is the main part of de-noising. For image de-noising many techniques are proposed by researchers and they have their own demerits and merits. In this paper, a denoising technique is introduced using Dual-Tree Complex Wavelet Transform (DT-CWT) based implementation with the Bio Inspired Optimization Algorithm and Artificial Neural Network (ANN). Initially, various sub-bands are generated from the noisy image by applying DT-CWT. Then a trained ANN is applied on every sub-bands to obtain the noise-free wavelet coefficients. Threshold operation is performed on these output wavelet coefficients with Grey wolf optimization Algorithm (GWA) based optimized threshold value. By applying inverse DT-CWT the noise-free image can be obtained. This approach shows better performance when compared to other conventional methods.
Actively Secure Half-Gates with Minimum Overhead Under Duplex Networks

Actively secure two-party computation (2PC) is one of the canonical building blocks in modern cryptography. One main goal for designing actively secure 2PC protocols is to reduce the communication overhead, compared to semi-honest 2PC protocols. In this paper, we propose a new actively secure constant-round 2PC protocol with one-way communication of $$2\kappa +5$$ bits per AND gate (for $$\kappa $$ -bit computational security and any statistical security), essentially matching the one-way communication of semi-honest half-gates protocol. This is achieved by two new techniques: Our technique of yielding authenticated AND triples can also be used to optimize the two-way communication (i.e., the total communication) by combining it with the authenticated garbled circuits by Dittmer et al., which results in an actively secure 2PC protocol with two-way communication of $$2\kappa +3\rho +4$$ bits per AND gate.
Evaluating Morphometric Feature Variability of Handwritten Numerals Among Malaysian Malays Using Self-organizing Maps

Forensic handwriting examination involves the determination of authorship of the questioned document encountered in a criminal case. However, the examination process is labor intensive and experience demanding. Understanding the variability of morphometric features of handwritten numerals would contribute to constructing predictive models in determining the authorship of a questioned document. Hence, this work aims to evaluate the variability of handwritten numerals based on morphometric features and self-organizing maps (SOMs). A total of 420 numerals (i.e., samples) were obtained from five Malaysian Malay subjects. Each subject wrote numerals 0–9 six times over 14 consecutive days using the same pen and paper type. At least two morphometric features were measured on each numeral resulting in 33 features. All the features were then converted into 44 ratio values. Missing values were replaced by the mean imputation method. The features contributing to the most variability among the five subjects were determined based on SOMs’ plots. Results showed that numerals 0–2, and 4–6 presented clustering by some subjects. Meanwhile, the other numerals showed no evident separation among the subjects. In conclusion, it seems feasible to construct a predictive model using numerals 0–2, and 4–6 to discriminate writers for forensic investigation.
Effective Vehicle Classification and Re-identification on Stanford Cars Dataset Using Convolutional Neural Networks

In recent years video surveillance has gained significant attention due to its versatile applications in security and traffic flow management. Vehicle Re-Identification (Re-ID) is a complicated task in Intelligent Transportation Systems (ITS) where vehicles are tracked by multiple cameras in non-overlapping views. In re-identification the targeted vehicle is matched with the query image from the gallery of vehicle images. There are many uncertainties involved in Re-ID due to inter- class and intra-class similarity because of the viewpoint changes. Large number of studies have explored the deep learning techniques for vehicle detection but classification of vehicle images and re-identifying them is still a field of exploration. In this paper we have analyzed the issue of fine grained vehicle classification and thereby re-identifying them using different CNN models. Vehicle images are pre-processed by different data augmentation techniques and then transfer learning is applied for testing the Stanford cars dataset on three models namely ResNet152, ResNet50 and DenseNet201. The accuracy is obtained for all the models and ResNet152 produces a highest accuracy of 89% among the three models.
On Valiant’s Conjecture

In his landmark paper at TCC 2008 Paul Valiant introduced the notion of “incrementally verifiable computation” which enables a prover to incrementally compute a succinct proof of correct execution of a (potentially) long running process. The paper later won the 2019 TCC test of time award. The construction was proven secure in the random oracle model without any further computational assumptions. However, the overall proof was given using a non-standard version of the random-oracle methodology where sometimes the hash function is a random oracle and sometimes it has a short description as a circuit. Valiant clearly noted that this model is non-standard, but conjectured that the standard random oracle methodology would not suffice. This conjecture has been open for 14 years. We prove that if the proof system can receive a long witness as input in an incremental manner and is also zero-knowledge then the conjecture is true. Valiant’s original construction does not have these properties but can easily be extended to have them in his model. We relate our result to recent possibility and impossibility results for SNARKs and incrementally verifiable computation.
XOCB: Beyond-Birthday-Bound Secure Authenticated Encryption Mode with Rate-One Computation

We present a new block cipher mode of operation for authenticated encryption (AE), dubbed $$\textsf{XOCB}$$ , that has the following features: (1) beyond-birthday-bound (BBB) security based on the standard pseudorandom assumption of the internal block cipher if the maximum block length is sufficiently smaller than the birthday bound, (2) rate-1 computation, and (3) supporting any block cipher with any key length. Namely, $$\textsf{XOCB}$$ has effectively the same efficiency as the seminal $$\textsf{OCB}$$ while having stronger quantitative security without any change in the security model or the required primitive in $$\textsf{OCB}$$ . Although numerous studies have been conducted in the past, our $$\textsf{XOCB}$$ is the first mode of operation to achieve these multiple goals simultaneously.
Stability Analysis of Continuous-Time Recurrent Neural Networks by IQC with Copositive Multipliers

This paper addresses the stability analysis problem of recurrent neural networks (RNNs) with activation functions being rectified linear units (ReLUs). In particular, we focus on the linear nonegative properties of the input-output signals of ReLUs. To capture these linear properties within the framework of integral quadratic constraint (IQC), we introduce a copositive multiplier constructed from a copositive matrix. This enables us to capture the linear input-output properties of ReLUs in quadratic form. By using the copositive multipliers in additon to existing multipliers, we can reduce the conservatism of the IQC-based stability analysis for RNNs. We also show that the proposed IQC-based stability condition with copositive multipliers can be viewed as an extension of a recently proposed scaled small-gain stability condition based on the L2+ induced norm.
Text-Enhanced Scene Image Super-Resolution via Stroke Mask and Orthogonal Attention

Low-resolution text images are very commonplace in real life and their information is hard to be extracted by using existing text recognition methods only. Although this problem can be solved by introducing super-resolution (SR) techniques, most existing SR methods fail to process stroke regions and background regions of input text images distinctively. In this paper, we propose a text-specific super-resolution network named Text Enhanced Attention Network (TEAN) to solve this problem. First of all, we compensate for disadvantages of traditional thresholding mask operation proposed in Text Super-Resolution Network (TSRN) by utilizing deep-learning based semantic segmentation method to get correct masks as prior semantic information and propose a Text-Segmented-Contextual-Attention (TSCA) branch on the basis of them. Besides, we design an Orthogonal Contextual Attention Module (OCAM) working with TSCA to implicitly enhance stroke regions of LR images. Secondly, to effectively fuse shallow features and deep features of SR model, we propose a convolutional structure named Weight Balanced Fusion Module (WBFM) to improve traditional feature fusion methods of SR network. Finally, extensive experiments on TextZoom dataset demonstrate that the proposed network can improve the recognition accuracy of text images on existing text recognition models. Using TEAN to process low-resolution text images improves the recognition accuracy by 25.4% on CRNN, by 17.4% on ASTER, by 17.3% on MORAN, by 20.7% on NRTR, by 17.3% on SAR and by 15.9% on MASTER compared with directly recognizing them, which attains competitive performances against state-of-the-art methods. Furthermore, cross-dataset experiments on IC15_2077 demonstrate that TEAN is helpful for scene text recognition task, especially for low-resolution images even with the cross-domain issue.
A Cross-Lingual Hybrid Neural Network With Interaction Enhancement for Grading Short-Answer Texts

Automatic Short-Answer Grading (ASAG) is an application for recognizing textual entailment in smart education. With the continuous expansion of the application scope of artificial neural networks, many deep learning models have been applied to grading short-answer texts. However, the coding structures and interaction forms of existing models are still too simple to meet the requirements of the ASAG task, resulting in low scoring accuracy. To address these challenges, we propose a cross-lingual hybrid neural network with interaction enhancement for ASAG. First, we sequentially use a convolutional neural network and bidirectional Long Short-Term Memory (LSTM) network to encode the answer text. Then, we introduce an interaction enhancement layer consisting of reference-answer-to-student-answer and student-answer-to-reference-answer attentions, and we combine the attentions and their inputs to form enhanced representations of answer texts. Finally, we introduce two Siamese Bi-LSTM networks to fuse the enhanced representations of answer texts and combine their multiple pooled vectors for grade classification on a multi-linear prediction layer. The experimental results show that our model significantly improves the performance of various simple models for Chinese and English ASAG tasks. The code is available online at https://github.com/wuhan-1222/DL_ASAG.
Security Enhancement Using Cryptography in Cloud-Based Education Portals

There are several means for learners to acquire education online from remote regions, but online education is rapidly expanding. The security of digital files during cloud transmission remains a concern. However, it was underlined that digital information security must be improved. The present research focuses on improving the security and efficiency of cloud-based remote learning systems. To provide remote learning, many types of cloud computing research have been undertaken. Performance and data security have been issues in past studies. A method that can safely transmit instructional information from one location to another in less time is required. The educational contents must be safeguarded and compressed during data transfer. In order to reduce the packet's size, researchers used a content substitution strategy. Because existing methods of encryption, such as advanced encryption standard (AES) and data encryption standard (DES), are time-consuming, the suggested work has increased security by using the substitution mechanism exclusively-OR. Because data is compressed and encrypted on the sender side before being sent, the technique is both secure and fast. During packet transmission, the suggested work has offered a method for dealing with packet loss, faults, packet hijacking, and a variety of other threats. After receiving, data is encrypted and decompressed at the receiver end. Data is encrypted after compression, the likelihood of breaking encrypted files decreases. RSA and DNA cryptography mechanisms are compared to the proposed method. Attacks on packets have been simulated in the case of the proposed work, RSA- and DNA-based cryptography. The simulation results show that the suggested method is more secure than existing traditional work and DNA cryptography techniques.
Learning General Descriptors for Image Matching With Regression Feedback

Recent advances on feature descriptors for image matching put more emphasis on encoding invariances (e.g. illumination invariance) to promote the descriptors’ discriminative power. However, according to the information entropy, more invariance implies greater certainty and less informativeness in a descriptor. Consequently, descriptors encoding too many invariances usually show poor generalization to unknown image changes, lacking enough informativeness to cover the large uncertainty in unseen scenes. This limits the application scenarios of learned descriptors. In this paper, we propose to alleviate this issue from the perspective of informativeness and we thus design hierarchical consistent constraint by introducing regression feedback in a self-supervised manner. Combined with the hardest-within-batch matching constraint, we form a novel dual supervision framework, to encourage the descriptor to learn an informative representation while maintaining a good discriminative power. Moreover, to fully mine the context information hidden in image and boost the informativeness in turn, we present AANet, a descriptor network that efficiently predicts dense description by the powerful Attentional Aggregation of multi-level features. Experiments across challenging feature matching on HPatches, RDNIM datasets, and visual localization tasks on Aachen Day-night dataset show that our method outperforms recent state-of-the-art descriptors while keeping encouraging efficiency. The application of visual 3D reconstruction on various scenarios also demonstrates the high generalization ability of our method.
A Theory of Composition for Differential Obliviousness

Differential obliviousness (DO) is a privacy notion which guarantees that the access patterns of a program satisfies differential privacy. Differential obliviousness was studied in a sequence of recent works as a relaxation of full obliviousness. Earlier works showed that DO not only allows us to circumvent the logarithmic-overhead barrier of fully oblivious algorithms, in many cases, it also allows us to achieve polynomial speedup over full obliviousness, since it avoids “padding to the worst-case” behavior of fully oblivious algorithms. Despite the promises of differential obliviousness (DO), a significant barrier that hinders its broad application is the lack of composability. In particular, when we apply one DO algorithm to the output of another DO algorithm, the composed algorithm may no longer be DO (with reasonable parameters). Specifically, the outputs of the first DO algorithm on two neighboring inputs may no longer be neighboring, and thus we cannot directly benefit from the DO guarantee of the second algorithm. In this work, we are the first to explore a theory of composition for differentially oblivious algorithms. We propose a refinement of the DO notion called $$(\epsilon , \delta )$$ -neighbor-preserving-DO, or $$(\epsilon , \delta )$$ -NPDO for short, and we prove that our new notion indeed provides nice compositional guarantees. In this way, the algorithm designer can easily track the privacy loss when composing multiple DO algorithms. We give several example applications to showcase the power and expressiveness of our new NPDO notion. One of these examples is a result of independent interest: we use the compositional framework to prove an optimal privacy amplification theorem for the differentially oblivious shuffle model. In other words, we show that for a class of distributed differentially private mechanisms in the shuffle-model, one can replace the perfectly secure shuffler with a DO shuffler, and nonetheless enjoy almost the same privacy amplification enabled by a shuffler.
Actively Secure Arithmetic Computation and VOLE with Constant Computational Overhead

We study the complexity of two-party secure arithmetic computation where the goal is to evaluate an arithmetic circuit over a finite field $$\mathbb {F}$$ in the presence of an active (aka malicious) adversary. In the passive setting, Applebaum et al. (Crypto 2017) constructed a protocol that only makes a constant (amortized) number of field operations per gate. This protocol uses the underlying field $$\mathbb {F}$$ as a black box, makes black-box use of (standard) oblivious transfer, and its security is based on arithmetic analogs of well-studied cryptographic assumptions. We present an actively-secure variant of this protocol that achieves, for the first time, all the above features. The protocol relies on the same assumptions and adds only a minor overhead in computation and communication. Along the way, we construct a highly-efficient Vector Oblivious Linear Evaluation (VOLE) protocol and present several practical and theoretical optimizations, as well as a prototype implementation. Our most efficient variant can achieve an asymptotic rate of 1/4 (i.e., for vectors of length w we send roughly 4w elements of $$\mathbb {F}$$ ), which is only slightly worse than the passively-secure protocol whose rate is 1/3. The protocol seems to be practically competitive over fast networks, even for relatively small fields $$\mathbb {F}$$ and relatively short vectors. Specifically, our VOLE protocol has 3 rounds, and even for 10K-long vectors, it has an amortized cost per entry of less than 4 OT’s and less than 300 arithmetic operations. Most of these operations (about 200) can be pre-processed locally in an offline non-interactive phase. (Better constants can be obtained for longer vectors.) Some of our optimizations rely on a novel intractability assumption regarding the non-malleability of noisy linear codes, that may be of independent interest. Our technical approach employs two new ingredients. First, we present a new information-theoretic construction of Conditional Disclosure of Secrets (CDS) and show how to use it in order to immunize the VOLE protocol of Applebaum et al. against active adversaries. Second, by using elementary properties of low-degree polynomials, we show that, for some simple arithmetic functionalities, one can easily upgrade Yao’s garbled-circuit protocol to the active setting with a minor overhead while preserving the round complexity.
Machine Learning-Based Tool to Classify Online Toxic Comments

Nowadays, on the internet, something unusual is happening. Abusive behaviors towards people on social media, as well as these online abominations, have real-life implications, such as the spread of fear and hatred through comments. Trolls on the internet tend to use derogatory language. In a nutshell, online prejudice has aided the spread of hatred. Toxic words are only effective because they can implant discriminatory beliefs without being noticed. To solve this problem, a Machine Learning-Based Tool to Classify Online Toxic Comment is proposed which uses seven machine learning algorithms, including Random Forest, KNN, SVM, Logistic Regression, Decision Tree, Naive Bayes, and Hybrid Algorithm, and apply them to input data to solve the problem of text classification and identify the best machine learning algorithm by using evaluation metrics for toxic comments classification by the accuracy comparison graph. The proposed model also predicts whether the given input comment is Toxic or Non-Toxic.
Backdoor Attacks to Deep Learning Models and Countermeasures: A Survey

Backdoor attacks have severely threatened deep neural network (DNN) models in the past several years.In backdoor attacks, the attackers try to plant hidden backdoors into DNN models, either in the training or inference stage, to mislead the output of the model when the input contains some specified triggers without affecting the prediction of normal inputs not containing the triggers.As a rapidly developing topic, numerous works on designing various backdoor attacks and developing techniques to defend against such attacks have been proposed in recent years.However, a comprehensive and holistic overview of backdoor attacks and countermeasures is still missing.In this paper, we provide a systematic overview of the design of backdoor attacks and the defense strategies to defend against backdoor attacks, covering the latest published works.We review representative backdoor attacks and defense strategies in both the computer vision domain and other domains, discuss their pros and cons, and make comparisons among them.We outline key challenges to be addressed and potential research directions in the future.