Reviewerly/exHarmony huggingface dataset:
Robustness of entanglement-based discrete- and continuous-variable quantum key distribution against channel noise

Abstract Discrete-variable (DV) and continuous-variable (CV) schemes constitute the two major families of quantum key distribution (QKD) protocols. Unfortunately, since the setup elements required by these schemes are quite different, making a fair comparison of their potential performance in particular applications is often troublesome, limiting the experimenters’ capability to choose an optimal solution. In this work we perform a general comparison of the major entanglement-based DV and CV QKD protocols in terms of their resistance to the channel noise, with the otherwise perfect setup, showing the definite superiority of the DV family. We analytically derive fundamental bounds on the tolerable channel noise and attenuation for entanglement-based CV QKD protocols. We also investigate the influence of DV QKD setup imperfections on the obtained results in order to determine benchmarks for the parameters of realistic photon sources and detectors, allowing the realistic DV protocols to outperform even the ideal CV QKD analogs. Our results indicate the realistic advantage of DV entanglement-based schemes over their CV counterparts and suggests the practical efforts for maximizing this advantage.
Research on Image Super-Resolution Reconstruction Technology Based on Unsupervised Learning

Affected by the movement of drones, missiles, and other aircraft platforms and the limitation of the accuracy of image sensors, the obtained images have low-resolution and serious loss of image details. Aiming at these problems, this paper studies the image super-resolution reconstruction technology. Firstly, a natural image degradation model based on a generative adversarial network is designed to learn the degradation relationship between image blocks within the image; then, an unsupervised learning residual network is designed based on the idea of image self-similarity to complete image super-resolution reconstruction. The experimental results show that the unsupervised super-resolution reconstruction algorithm is equivalent to the mainstream supervised learning algorithm under ideal conditions. Compared to mainstream algorithms, this algorithm has significantly improved its various indicators in real-world environments under nonideal conditions.
Region Normalized Capsule Network Based Generative Adversarial Network for Non-parallel Voice Conversion

Voice conversion (VC) involves altering the vocal characteristics of a source speaker to resemble those of a target speaker while maintaining the same linguistic content. Recently, researchers have turned to deep generative models, particularly generative adversarial network (GAN) models, for VC studies due to their superior performance compared to statistical models. However, there is a noticeable disparity in naturalness between real speech samples and those generated by state-of-the-art (SOTA) VC models. This study introduces an enhanced GAN model for non-parallel VC, which employs mel-spectrograms as the speech feature. The enhanced GAN model incorporates a region normalization technique in the generator and a discriminator based on capsule networks (Caps-Net), to improve the quality of the generated speech samples. The proposed model is evaluated using the VCC 2018 and CMU Arctic datasets. The experimental outcomes demonstrate that the region normalization technique-based Caps-Net GAN (RNCapsGAN-VC) model outperforms the SOTA MaskCycleGAN-VC model in terms of both objective and subjective evaluations considering less training time.
Optimizing and interpreting the latent space of the conditional text-to-image GANs

Abstract Text-to-image generation intends to automatically produce a photo-realistic image, conditioned on a textual description. To facilitate the real-world applications of text-to-image synthesis, we focus on studying the following three issues: (1) How to ensure that generated samples are believable, realistic or natural? (2) How to exploit the latent space of the generator to edit a synthesized image? (3) How to improve the explainability of a text-to-image generation framework? We introduce two new data sets for benchmarking, i.e., the Good &amp; Bad , bird and face, data sets consisting of successful as well as unsuccessful generated samples. This data set can be used to effectively and efficiently acquire high-quality images by increasing the probability of generating Good latent codes with a separate, new classifier. Additionally, we present a novel algorithm which identifies semantically understandable directions in the latent space of a conditional text-to-image GAN architecture by performing independent component analysis on the pre-trained weight values of the generator. Furthermore, we develop a background-flattening loss (BFL), to improve the background appearance in the generated images. Subsequently, we introduce linear-interpolation analysis between pairs of text keywords. This is extended into a similar triangular ‘linguistic’ interpolation. The visual array of interpolation results gives users a deep look into what the text-to-image synthesis model has learned within the linguistic embeddings. Experimental results on the recent DiverGAN generator, pre-trained on three common benchmark data sets demonstrate that our classifier achieves a better than 98% accuracy in predicting Good/Bad classes for synthetic samples and our proposed approach is able to derive various interpretable semantic properties for the text-to-image GAN model.
A Survey of Object Detection Methods in Inclement Weather Conditions

In recent years, significant progress has been achieved in object detection based on Deep Neural Netwroks (DNN). However, the object detecting in inclement weather conditions remains a challenging problem, as the performance of certain sensor degrades. In unfavorable weather conditions such as haze, rainy day and snow day, the sensor information obtained from the environment becomes more ambiguous, presenting numerous difficulties for subsequent object detection tasks. This survey provides a comprehensive overview of the recent advances in the field of computer vision regarding object detection tasks in inclement weather conditions. The article begins by describing, comparing, and analyzing existing relevant databases. It then reviews image-based object detection algorithms specifically de-signed to address the challenges posed by inclement weather conditions. Finally, it discusses the methods and applications of multi-modal data-based object detection approaches in inclement weather conditions. The article also discusses future works and potential areas of research in this field.
AntiFake: Using Adversarial Audio to Prevent Unauthorized Speech Synthesis

The rapid development of deep neural networks and generative AI has catalyzed growth in realistic speech synthesis. While this technology has great potential to improve lives, it also leads to the emergence of ''DeepFake'' where synthesized speech can be misused to deceive humans and machines for nefarious purposes. In response to this evolving threat, there has been a significant amount of interest in mitigating this threat by DeepFake detection.
Formalizing, Verifying and Applying ISA Security Guarantees as Universal Contracts

Progress has recently been made on specifying instruction set architectures (ISAs) in executable formalisms rather than through prose. However, to date, those formal specifications are limited to the functional aspects of the ISA and do not cover its security guarantees. We present a novel, general method for formally specifying an ISA's security guarantees to (1) balance the needs of ISA implementations (hardware) and clients (software), (2) can be semi-automatically verified to hold for the ISA operational semantics, producing a high-assurance mechanically-verifiable proof, and (3) support informal and formal reasoning about security-critical software in the presence of adversarial code. Our method leverages universal contracts: software contracts that express bounds on the authority of arbitrary untrusted code. Universal contracts can be kept agnostic of software abstractions, and strike the right balance between requiring sufficient detail for reasoning about software and preserving implementation freedom of ISA designers and CPU implementers. We semi-automatically verify universal contracts against Sail implementations of ISA semantics using our Katamaran tool; a semi-automatic separation logic verifier for Sail which produces machine-checked proofs for successfully verified contracts. We demonstrate the generality of our method by applying it to two ISAs that offer very different security primitives: (1) MinimalCaps: a custom-built capability machine ISA and (2) a (somewhat simplified) version of RISC-V with PMP. We verify a femtokernel using the security guarantee we have formalized for RISC-V with PMP.
Waks-On/Waks-Off: Fast Oblivious Offline/Online Shuffling and Sorting with Waksman Networks

As more privacy-preserving solutions leverage trusted execution environments (TEEs) like Intel SGX, it becomes pertinent that these solutions can by design thwart TEE side-channel attacks that research has brought to light. In particular, such solutions need to be fully oblivious to circumvent leaking private information through memory or timing side channels.
Interchain Timestamping for Mesh Security

Fourteen years after the invention of Bitcoin, there has been a proliferation of many permissionless blockchains. Each such chain provides a public ledger that can be written to and read from by anyone. In this multi-chain world, a natural question arises: what is the optimal security an existing blockchain, a consumer chain, can extract by only reading and writing to k other existing blockchains, the provider chains? We design a protocol, called interchain timestamping, and show that it extracts the maximum economic security from the provider chains, as quantified by the slashable safety resilience. We observe that interchain timestamps are already provided by light-client based bridges, so interchain timestamping can be readily implemented for Cosmos chains connected by the Inter-Blockchain Communication (IBC) protocol. We compare interchain timestamping with cross-staking, the original solution to mesh security, as well as with Trustboost, another recent security sharing protocol.
SysXCHG: Refining Privilege with Adaptive System Call Filters

We present the design, implementation, and evaluation of SysXCHG: a system call (syscall) filtering enforcement mechanism that enables programs to run in accordance with the principle of least privilege. In contrast to the current, hierarchical design of seccomp-BPF, which does not allow a program to run with a different set of allowed syscalls than its descendants, SysXCHG enables applications to run with "tight" syscall filters, uninfluenced by any future-executed (sub-)programs, by allowing filters to be dynamically exchanged at runtime during execve[at]. As a part of SysXCHG, we also present xfilter: a mechanism for fast filtering using a process-specific view of the kernel's syscall table where filtering is performed. In our evaluation of SysXCHG, we found that our filter exchanging design is performant, incurring ≤= 1.71% slowdown on real-world programs in the PaSH benchmark suite, as well as effective, blocking vast amounts of extraneous functionality, including security-critical syscalls, which the current design of seccomp-BPF is unable to.
Realistic Website Fingerprinting By Augmenting Network Traces

Website Fingerprinting (WF) is considered a major threat to the anonymity of Tor users (and other anonymity systems). While state-of-the-art WF techniques have claimed high attack accuracies, e.g., by leveraging Deep Neural Networks (DNN), several recent works have questioned the practicality of such WF attacks in the real world due to the assumptions made in the design and evaluation of these attacks. In this work, we argue that such impracticality issues are mainly due to the attacker's inability in collecting training data in comprehensive network conditions, e.g., a WF classifier may be trained only on high-bandwidth samples collected on specific high-bandwidth network links but deployed on connections with different network conditions. We show that augmenting network traces can enhance the performance of WF classifiers in unobserved network conditions. Specifically, we introduce NetAugment, an augmentation technique tailored to the specifications of Tor traces. We instantiate NetAugment through semi-supervised and self-supervised learning techniques. Our extensive open-world and close-world experiments demonstrate that under practical evaluation settings, our WF attacks provide superior performances compared to the state-of-the-art; this is due to their use of augmented network traces for training, which allows them to learn the features of target traffic in unobserved settings (e.g., unknown bandwidth, Tor circuits, etc.). For instance, with a 5-shot learning in a closed-world scenario, our self-supervised WF attack (named NetCLR) reaches up to 80% accuracy when the traces for evaluation are collected in a setting unobserved by the WF adversary. This is compared to an accuracy of 64.4% achieved by the state-of-the-art Triplet Fingerprinting [34]. We believe that the promising results of our work can encourage the use of network trace augmentation in other types of network traffic analysis.
Stealing the Decoding Algorithms of Language Models

A key component of generating text from modern language models (LM) is the selection and tuning of decoding algorithms. These algorithms determine how to generate text from the internal probability distribution generated by the LM. The process of choosing a decoding algorithm and tuning its hyperparameters takes significant time, manual effort, and computation, and it also requires extensive human evaluation. Therefore, the identity and hyperparameters of such decoding algorithms are considered to be extremely valuable to their owners. In this work, we show, for the first time, that an adversary with typical API access to an LM can steal the type and hyperparameters of its decoding algorithms at very low monetary costs. Our attack is effective against popular LMs used in text generation APIs, including GPT-2, GPT-3 and GPT-Neo. We demonstrate the feasibility of stealing such information with only a few dollars, e.g., 0.8, 1, 4, and 40 for the four versions of GPT-3.
COMBINE: COMpilation and Backend-INdependent vEctorization for Multi-Party Computation

Recent years have witnessed significant advances in programming technology for multi-party computation (MPC), bringing MPC closer to practice and wider applicability. Typical MPC programming frameworks focus on either front-end language design (e.g., Wysteria, Viaduct, SPDZ), or back-end protocol design and implementation (e.g., ABY, MOTION, MP-SPDZ).
Prediction Privacy in Distributed Multi-Exit Neural Networks: Vulnerabilities and Solutions

Distributed Multi-exit Neural Networks (MeNNs) use partitioning and early exits to reduce the cost of neural network inference on low-power sensing systems. Existing MeNNs exhibit high inference accuracy using policies that select when to exit based on data-dependent prediction confidence. This paper presents a side-channel attack against distributed MeNNs employing data-dependent early exit policies. We find that an adversary can observe when a distributed MeNN exits early using encrypted communication patterns. An adversary can then use these observations to discover the MeNN's predictions with over 1.85× the accuracy of random guessing. In some cases, the side-channel leaks over 80% of the model's predictions. This leakage occurs because prior policies make decisions using a single threshold on varying prediction confidence distributions. We address this problem through two new exit policies. The first method, Per-Class Exiting (PCE), uses multiple thresholds to balance exit rates across predicted classes. This policy retains high accuracy and lowers prediction leakage, but we prove it has no privacy guarantees. We obtain these guarantees with a second policy, Confidence-Guided Randomness (CGR), which randomly selects when to exit using probabilities biased toward PCE's decisions. CGR provides statistically equivalent privacy with consistently higher inference accuracy than exiting early uniformly at random. Both PCE and CGR have low overhead, making them viable security solutions in resource-constrained settings.
Evaluating the Security Posture of Real-World FIDO2 Deployments

FIDO2 is a suite of protocols that combines the usability of local authentication (e.g., biometrics) with the security of public-key cryptography to deliver passwordless authentication. It eliminates shared authentication secrets (i.e., passwords, which could be leaked or phished) and provides strong security guarantees assuming the benign behavior of the client-side protocol components.
Post-Quantum Multi-Recipient Public Key Encryption

A multi-message multi-recipient PKE (mmPKE) encrypts a batch of messages, in one go, to a corresponding set of independently chosen receiver public keys. The resulting ''multi-recipient ciphertext'' can be then be reduced (by any 3rd party) to a shorter, receiver specific, ''invidual ciphertext.'' Finally, to recover the i-th message in the batch from their indvidual ciphertext the i-th receiver only needs their own decryption key. A special case of mmPKE is multi-recipient PKE (mPKE) where all receivers are sent the same message. By treating (m)mPKE and their KEM counterparts as a stand-alone primitives we allow for more efficient constructions than trivially composing individual PKE/KEM instances. This is especially valuable in the post-quantum setting, where PKE/KEM ciphertexts and public keys tend to be far larger than their classic counterparts.
Recursion over Public-Coin Interactive Proof Systems; Faster Hash Verification

SNARK is a well-known family of cryptographic tools that is increasingly used in the field of computation integrity at scale. In this area, multiple works have introduced SNARK-friendly cryptographic primitives: hashing, but also encryption and signature verification. Despite all the efforts to create cryptographic primitives that can be proved faster, it remains a major performance hole in practice. In this paper, we present a recursive technique that can improve the efficiency of the prover by an order of magnitude compared to proving MiMC hashes (a SNARK-friendly hash function, Albrecht et al. 2016) with a Groth16 (Eurocrypt 2016) proof. We use GKR (a well-known public-coin argument system by Goldwasser et al., STOC 2008) to prove the integrity of hash computations and embed the GKR verifier inside a SNARK circuit. The challenge comes from the fact that GKR is a public-coin interactive protocol, and applying Fiat-Shamir naively may result in worse performance than applying existing techniques directly. This is because Fiat-Shamir itself is involved with hash computation over a large string. We take advantage of a property that SNARK schemes commonly have, to build a protocol in which the Fiat-Shamir hashes have very short inputs. The technique we present is generic and can be applied over any SNARK-friendly hash, most known SNARK schemes, and any (one-round) public-coin argument system in place of GKR. We emphasize that while our general compiler is secure in the random oracle model, our concrete instantiation (i.e., GKR plus outer SNARK) is only proved to be heuristically secure. This is due to the fact we first need to convert the GKR protocol to a one-round protocol. Thus, the random oracle of GKR, starting from the second round, is replaced with a concrete hash inside the outer layer SNARK which makes the security-proof heuristic.
"We've Disabled MFA for You": An Evaluation of the Security and Usability of Multi-Factor Authentication Recovery Deployments

Multi-Factor Authentication is intended to strengthen the security of password-based authentication by adding another factor, such as hardware tokens or one-time passwords using mobile apps.
Specification and Verification of Side-channel Security for Open-source Processors via Leakage Contracts

Leakage contracts have recently been proposed as a new security abstraction at the Instruction Set Architecture (ISA) level. Leakage contracts aim to capture the information that processors leak through their microarchitectural implementations. However, so far, we lack a methodology to verify that a processor actually satisfies a given leakage contract.
ProvG-Searcher: A Graph Representation Learning Approach for Efficient Provenance Graph Search

We present ProvG-Searcher, a novel approach for detecting known APT behaviors within system security logs. Our approach leverages provenance graphs, a comprehensive graph representation of event logs, to capture and depict data provenance relations by mapping system entities as nodes and their interactions as edges. We formulate the task of searching provenance graphs as a subgraph matching problem and employ a graph representation learning method. The central component of our search methodology involves embedding of subgraphs in a vector space where subgraph relationships can be directly evaluated. We achieve this through the use of order embeddings that simplify subgraph matching to straightforward comparisons between a query and precomputed subgraph representations. To address challenges posed by the size and complexity of provenance graphs, we propose a graph partitioning scheme and a behavior-preserving graph reduction method. Overall, our technique offers significant computational efficiency, allowing most of the search computation to be performed offline while incorporating a lightweight comparison step during query execution. Experimental results on standard datasets demonstrate that ProvG-Searcher achieves superior performance, with an accuracy exceeding 99% in detecting query behaviors and a false positive rate of approximately 0.02%, outperforming other approaches.