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Anindya Maiti

Publications and source records attributed to Anindya Maiti.

At least 19 recordsLinked to original sources

Block-A-Mole: The Sustainability Frontier of Moving-Target Censorship Resistance

Internet censorship affects over four billion people, and deployed circumvention systems share a common weakness: their endpoints are fixed and discoverable, so a patient censor can enumerate and block them. Moving-target circumvention systems instead rotate endpoints across commercial cloud address space faster than censors can react, but the field lacks a theory of when rotation works, leaving rotation intervals and pool sizes to intuition. We give the first formal account of moving-target censorship resistance by modeling the censor-defender interaction as a continuous-time timing game over a combinatorial address-domain space, generalizing FlipIt to a collateral-bounded adversary. We prove a sustainability frontier separating configurations a censor can defeat from those it cannot, and show that under the Great Firewall's 2024 shift to blocking QUIC and TLS by domain, raw rotation speed is not the binding constraint. Instead, availability is governed by the domain burn rate, $\beta=\lambda_{\mathrm{disc}}/\lambda_{\mathrm{intro}}$, the ratio between how quickly the censor blocks defender domains and how quickly the defender introduces fresh ones. We derive a closed-form availability law, prove that address rotation alone cannot sustain high availability when $\beta>1$ regardless of endpoint rotation speed, and characterize the frontier $\beta^\star$. We validate the analysis with an open, model-level censor-defender simulator requiring no privileged access or cloud deployment. The simulator reproduces the predicted phase transition at $\beta^\star$ under adversary profiles representative of the GFW, Russia's TSPU, and Iran, and shows robustness to state-dependent discovery and bursty, provider-correlated burns. The result replaces the heuristic of ``rotate faster'' with a precise operating condition: keeping the domain economy ahead of the censor.

cs.CR

CLPIPS: A Personalized Metric for AI-Generated Image Similarity

Iterative prompt refinement is central to reproducing target images with text to image generative models. Previous studies have incorporated image similarity metrics (ISMs) as additional feedback to human users. Existing ISMs such as LPIPS and CLIP provide objective measures of image likeness but often fail to align with human judgments, particularly in context specific or user driven tasks. In this paper, we introduce Customized Learned Perceptual Image Patch Similarity (CLPIPS), a customized extension of LPIPS that adapts a metric's notion of similarity directly to human judgments. We aim to explore whether lightweight, human augmented fine tuning can meaningfully improve perceptual alignment, positioning similarity metrics as adaptive components for human in the loop workflows with text to image tools. We evaluate CLPIPS on a human subject dataset in which participants iteratively regenerate target images and rank generated outputs by perceived similarity. Using margin ranking loss on human ranked image pairs, we fine tune only the LPIPS layer combination weights and assess alignment via Spearman rank correlation and Intraclass Correlation Coefficient. Our results show that CLPIPS achieves stronger correlation and agreement with human judgments than baseline LPIPS. Rather than optimizing absolute metric performance, our work emphasizes improving alignment consistency between metric predictions and human ranks, demonstrating that even limited human specific fine tuning can meaningfully enhance perceptual alignment in human in the loop text to image workflows.

cs.CV

Prompt and Circumstances: Evaluating the Efficacy of Human Prompt Inference in AI-Generated Art

The emerging field of AI-generated art has witnessed the rise of prompt marketplaces, where creators can purchase, sell, or share prompts to generate unique artworks. These marketplaces often assert ownership over prompts, claiming them as intellectual property. This paper investigates whether concealed prompts sold on prompt marketplaces can be considered bona fide intellectual property, given that humans and AI tools may be able to infer the prompts based on publicly advertised sample images accompanying each prompt on sale. Specifically, our study aims to assess (i) how accurately humans can infer the original prompt solely by examining an AI-generated image, with the goal of generating images similar to the original image, and (ii) the possibility of improving upon individual human and AI prompt inferences by crafting combined human and AI prompts with the help of a large language model. Although previous research has explored AI-driven prompt inference and protection strategies, our work is the first to incorporate a human subject study and examine collaborative human-AI prompt inference in depth. Our findings indicate that while prompts inferred by humans and prompts inferred through a combined human and AI effort can generate images with a moderate level of similarity, they are not as successful as using the original prompt. Moreover, combining human- and AI-inferred prompts using our suggested merging techniques did not improve performance over purely human-inferred prompts.

cs.CR

EtherBee: A Global Dataset of Ethereum Node Performance Measurements Coupled with Honeypot Interactions and Full Network Sessions

We introduce EtherBee, a global dataset integrating detailed Ethereum node metrics, network traffic metadata, and honeypot interaction logs collected from ten geographically diverse vantage points over three months. By correlating node data with granular network sessions and security events, EtherBee provides unique insights into benign and malicious activity, node stability, and network-level threats in the Ethereum peer-to-peer network. A case study shows how client-based optimizations can unintentionally concentrate the network geographically, impacting resilience and censorship resistance. We publicly release EtherBee to promote further investigations into performance, reliability, and security in decentralized networks.

cs.NI

A Picture is Worth a Thousand Prompts? Efficacy of Iterative Human-Driven Prompt Refinement in Image Regeneration Tasks

With AI-generated content becoming ubiquitous across the web, social media, and other digital platforms, it is vital to examine how such content are inspired and generated. The creation of AI-generated images often involves refining the input prompt iteratively to achieve desired visual outcomes. This study focuses on the relatively underexplored concept of image regeneration using AI, in which a human operator attempts to closely recreate a specific target image by iteratively refining their prompt. Image regeneration is distinct from normal image generation, which lacks any predefined visual reference. A separate challenge lies in determining whether existing image similarity metrics (ISMs) can provide reliable, objective feedback in iterative workflows, given that we do not fully understand if subjective human judgments of similarity align with these metrics. Consequently, we must first validate their alignment with human perception before assessing their potential as a feedback mechanism in the iterative prompt refinement process. To address these research gaps, we present a structured user study evaluating how iterative prompt refinement affects the similarity of regenerated images relative to their targets, while also examining whether ISMs capture the same improvements perceived by human observers. Our findings suggest that incremental prompt adjustments substantially improve alignment, verified through both subjective evaluations and quantitative measures, underscoring the broader potential of iterative workflows to enhance generative AI content creation across various application domains.

cs.AI

We Have a Package for You! A Comprehensive Analysis of Package Hallucinations by Code Generating LLMs

The reliance of popular programming languages such as Python and JavaScript on centralized package repositories and open-source software, combined with the emergence of code-generating Large Language Models (LLMs), has created a new type of threat to the software supply chain: package hallucinations. These hallucinations, which arise from fact-conflicting errors when generating code using LLMs, represent a novel form of package confusion attack that poses a critical threat to the integrity of the software supply chain. This paper conducts a rigorous and comprehensive evaluation of package hallucinations across different programming languages, settings, and parameters, exploring how a diverse set of models and configurations affect the likelihood of generating erroneous package recommendations and identifying the root causes of this phenomenon. Using 16 popular LLMs for code generation and two unique prompt datasets, we generate 576,000 code samples in two programming languages that we analyze for package hallucinations. Our findings reveal that that the average percentage of hallucinated packages is at least 5.2% for commercial models and 21.7% for open-source models, including a staggering 205,474 unique examples of hallucinated package names, further underscoring the severity and pervasiveness of this threat. To overcome this problem, we implement several hallucination mitigation strategies and show that they are able to significantly reduce the number of package hallucinations while maintaining code quality. Our experiments and findings highlight package hallucinations as a persistent and systemic phenomenon while using state-of-the-art LLMs for code generation, and a significant challenge which deserves the research community's urgent attention.

cs.SE

Why You've Got Mail: Evaluating Inbox Privacy Implications of Email Marketing Practices in Online Apps and Services

This study explores the widespread perception that personal data, such as email addresses, may be shared or sold without informed user consent, investigating whether these concerns are reflected in actual practices of popular online services and apps. Over the course of a year, we collected and analyzed the source, volume, frequency, and content of emails received by users after signing up for the 150 most popular online services and apps across various sectors. By examining patterns in email communications, we aim to identify consistent strategies used across industries, including potential signs of third-party data sharing. This analysis provides a critical evaluation of how email marketing tactics may intersect with data-sharing practices, with important implications for consumer privacy and regulatory oversight. Our study findings, conducted post-CCPA and GDPR, indicate that while no unknown third-party spam email was detected, internal and authorized third-party email marketing practices were pervasive, with companies frequently sending promotional and CRM emails despite opt-out preferences. The framework established in this work is designed to be scalable, allowing for continuous monitoring, and can be extended to include a more diverse set of apps and services for broader analysis, ultimately contributing to transparency in email address privacy practices.

cs.SI

MagnetDB: A Longitudinal Torrent Discovery Dataset with IMDb-Matched Movies and TV Shows

BitTorrent remains a prominent channel for illicit distribution of copyrighted material, yet the supply side of such content remains understudied. We introduce MagnetDB, a longitudinal dataset of torrents discovered through the BitTorrent DHT between 2018 and 2024, containing more than 28.6 million torrents and metadata of more than 950 million files. While our primary focus is on enabling research based on the supply of pirated movies and TV shows, the dataset also encompasses other legitimate and illegitimate torrents. By applying IMDb-matching and annotation to movie and TV show torrents, MagnetDB facilitates detailed analyses of pirated content evolution in the BitTorrent network. Researchers can leverage MagnetDB to examine distribution trends, subcultural practices, and the gift economy within piracy ecosystems. Through its scale and temporal scope, MagnetDB presents a unique opportunity for investigating the broader dynamics of BitTorrent and advancing empirical knowledge on digital piracy.

cs.CY

ScooterLab: A Programmable and Participatory Sensing Research Testbed using Micromobility Vehicles

Micromobility vehicles, such as e-scooters, are increasingly popular in urban communities but present significant challenges in terms of road safety, user privacy, infrastructure planning, and civil engineering. Addressing these critical issues requires a large-scale and easily accessible research infrastructure to collect diverse mobility and contextual data from micromobility users in realistic settings. To this end, we present ScooterLab, a community research testbed comprising a fleet of customizable battery-powered micromobility vehicles retrofitted with advanced sensing, communication, and control capabilities. ScooterLab enables interdisciplinary research at the intersection of computing, mobility, and urban planning by providing researchers with tools to design and deploy customized sensing experiments and access curated datasets. The testbed will enable advances in machine learning, privacy, and urban transportation research while promoting sustainable mobility.

cs.ET

Initial Evidence of Elevated Reconnaissance Attacks Against Nodes in P2P Overlay Networks

We hypothesize that peer-to-peer (P2P) overlay network nodes can be attractive to attackers due to their visibility, sustained uptime, and resource potential. Towards validating this hypothesis, we investigate the state of active reconnaissance attacks on Ethereum P2P network nodes by deploying a series of honeypots alongside actual Ethereum nodes across globally distributed vantage points. We find that Ethereum nodes experience not only increased attacks, but also specific types of attacks targeting particular ports and services. Furthermore, we find evidence that the threat assessment on our nodes is applicable to the wider P2P network by having performed port scans on other reachable peers. Our findings provide insights into potential mitigation strategies to improve the security of the P2P networking layer.

cs.CR

NinjaDoH: A Censorship-Resistant Moving Target DoH Server Using Hyperscalers and IPNS

We introduce NinjaDoH, a novel DNS over HTTPS (DoH) protocol that leverages the InterPlanetary Name System (IPNS), along with public cloud infrastructure, to create a censorship-resistant moving target DoH service. NinjaDoH is specifically designed to evade traditional censorship methods that involve blocking DoH servers by IP addresses or domains by continually altering the server's network identifiers, significantly increasing the complexity of effectively censoring NinjaDoH traffic without disruption of other web traffic. We also present an analysis that quantifies the DNS query latency and financial costs of running our implementation of this protocol as a service. Further tests assess the ability of NinjaDoH to elude detection mechanisms, including both commercial firewall products and advanced machine learning-based detection systems. The results broadly support NinjaDoH's efficacy as a robust, moving target DNS solution that can ensure continuous and secure internet access in environments with heavy DNS-based censorship.

cs.CR

Promptly Yours? A Human Subject Study on Prompt Inference in AI-Generated Art

The emerging field of AI-generated art has witnessed the rise of prompt marketplaces, where creators can purchase, sell, or share prompts for generating unique artworks. These marketplaces often assert ownership over prompts, claiming them as intellectual property. This paper investigates whether concealed prompts sold on prompt marketplaces can be considered as secure intellectual property, given that humans and AI tools may be able to approximately infer the prompts based on publicly advertised sample images accompanying each prompt on sale. Specifically, our survey aims to assess (i) how accurately can humans infer the original prompt solely by examining an AI-generated image, with the goal of generating images similar to the original image, and (ii) the possibility of improving upon individual human and AI prompt inferences by crafting human-AI combined prompts with the help of a large language model. Although previous research has explored the use of AI and machine learning to infer (and also protect against) prompt inference, we are the first to include humans in the loop. Our findings indicate that while humans and human-AI collaborations can infer prompts and generate similar images with high accuracy, they are not as successful as using the original prompt.

cs.HC

Spiking Neural Networks in Vertical Federated Learning: Performance Trade-offs

Federated machine learning enables model training across multiple clients while maintaining data privacy. Vertical Federated Learning (VFL) specifically deals with instances where the clients have different feature sets of the same samples. As federated learning models aim to improve efficiency and adaptability, innovative neural network architectures like Spiking Neural Networks (SNNs) are being leveraged to enable fast and accurate processing at the edge. SNNs, known for their efficiency over Artificial Neural Networks (ANNs), have not been analyzed for their applicability in VFL, thus far. In this paper, we investigate the benefits and trade-offs of using SNN models in a vertical federated learning setting. We implement two different federated learning architectures -- with model splitting and without model splitting -- that have different privacy and performance implications. We evaluate the setup using CIFAR-10 and CIFAR-100 benchmark datasets along with SNN implementations of VGG9 and ResNET classification models. Comparative evaluations demonstrate that the accuracy of SNN models is comparable to that of traditional ANNs for VFL applications, albeit significantly more energy efficient.

cs.LG

Towards a Game-theoretic Understanding of Explanation-based Membership Inference Attacks

Model explanations improve the transparency of black-box machine learning (ML) models and their decisions; however, they can also be exploited to carry out privacy threats such as membership inference attacks (MIA). Existing works have only analyzed MIA in a single "what if" interaction scenario between an adversary and the target ML model; thus, it does not discern the factors impacting the capabilities of an adversary in launching MIA in repeated interaction settings. Additionally, these works rely on assumptions about the adversary's knowledge of the target model's structure and, thus, do not guarantee the optimality of the predefined threshold required to distinguish the members from non-members. In this paper, we delve into the domain of explanation-based threshold attacks, where the adversary endeavors to carry out MIA attacks by leveraging the variance of explanations through iterative interactions with the system comprising of the target ML model and its corresponding explanation method. We model such interactions by employing a continuous-time stochastic signaling game framework. In our framework, an adversary plays a stopping game, interacting with the system (having imperfect information about the type of an adversary, i.e., honest or malicious) to obtain explanation variance information and computing an optimal threshold to determine the membership of a datapoint accurately. First, we propose a sound mathematical formulation to prove that such an optimal threshold exists, which can be used to launch MIA. Then, we characterize the conditions under which a unique Markov perfect equilibrium (or steady state) exists in this dynamic system. By means of a comprehensive set of simulations of the proposed game model, we assess different factors that can impact the capability of an adversary to launch MIA in such repeated interaction settings.

cs.AI

Generating Print-Ready Personalized AI Art Products from Minimal User Inputs

We present a novel framework to advance generative artificial intelligence (AI) applications in the realm of printed art products, specifically addressing large-format products that require high-resolution artworks. The framework consists of a pipeline that addresses two major challenges in the domain: the high complexity of generating effective prompts, and the low native resolution of images produced by diffusion models. By integrating AI-enhanced prompt generations with AI-powered upscaling techniques, our framework can efficiently produce high-quality, diverse artistic images suitable for many new commercial use cases. Our work represents a significant step towards democratizing high-quality AI art, opening new avenues for consumers, artists, designers, and businesses.

cs.CV

OverHear: Headphone based Multi-sensor Keystroke Inference

Headphones, traditionally limited to audio playback, have evolved to integrate sensors like high-definition microphones and accelerometers. While these advancements enhance user experience, they also introduce potential eavesdropping vulnerabilities, with keystroke inference being our concern in this work. To validate this threat, we developed OverHear, a keystroke inference framework that leverages both acoustic and accelerometer data from headphones. The accelerometer data, while not sufficiently detailed for individual keystroke identification, aids in clustering key presses by hand position. Concurrently, the acoustic data undergoes analysis to extract Mel Frequency Cepstral Coefficients (MFCC), aiding in distinguishing between different keystrokes. These features feed into machine learning models for keystroke prediction, with results further refined via dictionary-based word prediction methods. In our experimental setup, we tested various keyboard types under different environmental conditions. We were able to achieve top-5 key prediction accuracy of around 80% for mechanical keyboards and around 60% for membrane keyboards with top-100 word prediction accuracies over 70% for all keyboard types. The results highlight the effectiveness and limitations of our approach in the context of real-world scenarios.

cs.CR

Stake Your Claim: Zero-Trust Validator Deployment Leveraging NFTs and Smart Contracts in Proof-of-Stake Networks

We present a novel method for a multi-party, zero-trust validator infrastructure deployment arrangement via smart contracts to secure Proof-of-Stake (PoS) blockchains. The proposed arrangement architecture employs a combination of non-fungible tokens (NFTs), a treasury contract, and validator smart contract wallets to facilitate trustless participation in staking mechanisms. The NFT minting process allows depositors to exchange their capital for an NFT representing their stake in a validator, while the treasury contract manages the registry of NFT holders and handles rewards distribution. Validator smart contract wallets are employed to create a trustless connection between the validator operator and the treasury, enabling autonomous staking and unstaking processes based on predefined conditions. In addition, the proposed system incorporates protection mechanisms for depositors, such as triggered exits in case of non-payment of rewards and a penalty payout from the validator operator. The arrangement benefits from the extensibility and interoperability of web3 technologies, with potential applications in the broader digital ecosystem. This zero-trust staking mechanism aims to serve users who desire increased privacy, trust, and flexibility in managing their digital wealth, while promoting greater decentralization and transparency in the PoS ecosystem.

cs.CR

SkinSense: Efficient Vibration-based Communications Over Human Body Using Motion Sensors

Recent growth in popularity of mobile and wearable devices has re-ignited the need for reliable and stealthy communication side-channels to enable applications such as secret/PIN sharing, co-location proofs and user authentication. Existing short-range wireless radio technology such as Bluetooth/BLE and NFC, although mature and robust, is prone to eavesdropping, jamming and/or interference, and is not very useful as a covert communication side-channel. This paper designs and implements SkinSense, a vibration-based communication protocol which uses human body/skin as a communication medium to create a low-bandwidth and covert communication channel between user-held mobile and wearable devices. SkinSense employs a novel frequency modulation technique for encoding bits as vibration pulses and a spectrogram-based approach to decode the sensed motion data (corresponding to the encoded vibration pulses) to reconstruct the transmitted bits. SkinSense is comprehensively evaluated for a variety of operational parameters, hardware setups and communication settings by means of data collected from human subject participants. Results from these empirical evaluations demonstrate that SkinSense is able to achieve a stable bandwidth of upto 6.6 bps, with bit error rates below 0.1 in our custom hardware setup, and can be employed as a practical communication side-channel.

eess.SP