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Register a new userMaking Paper Reviewing Robust to Bid Manipulation Attacks
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Chuan Guo
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Most computer science conferences rely on paper bidding to assign reviewers to papers. Although paper bidding enables high-quality assignments in days of unprecedented submission numbers, it also opens the door for dishonest reviewers to adversarially influence paper reviewing assignments. Anecdotal evidence suggests that some reviewers bid on papers by friends or colluding authors, even though these papers are outside their area of expertise, and recommend them for acceptance without considering the merit of the work. In this paper, we study the efficacy of such bid manipulation attacks and find that, indeed, they can jeopardize the integrity of the review process. We develop a novel approach for paper bidding and assignment that is much more robust against such attacks. We show empirically that our approach provides robustness even when dishonest reviewers collude, have full knowledge of the assignment systems internal workings, and have access to the systems inputs. In addition to being more robust, the quality of our paper review assignments is comparable to that of current, non-robust assignment approaches.
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We study robust testing and estimation of discrete distributions in the strong contamination model. We consider both the centralized setting and the distributed setting with information constraints including communication and local privacy (LDP) constraints. Our technique relates the strength of manipulation attacks to the earth-mover distance using Hamming distance as the metric between messages(samples) from the users. In the centralized setting, we provide optimal error bounds for both learning and testing. Our lower bounds under local information constraints build on the recent lower bound methods in distributed inference. In the communication constrained setting, we develop novel algorithms based on random hashing and an $ell_1/ell_1$ isometry.
In this paper, we investigate the problem about how to bid in repeated contextual first price auctions. We consider a single bidder (learner) who repeatedly bids in the first price auctions: at each time $t$, the learner observes a context $x_tin mathbb{R}^d$ and decides the bid based on historical information and $x_t$. We assume a structured linear model of the maximum bid of all the others $m_t = alpha_0cdot x_t + z_t$, where $alpha_0in mathbb{R}^d$ is unknown to the learner and $z_t$ is randomly sampled from a noise distribution $mathcal{F}$ with log-concave density function $f$. We consider both emph{binary feedback} (the learner can only observe whether she wins or not) and emph{full information feedback} (the learner can observe $m_t$) at the end of each time $t$. For binary feedback, when the noise distribution $mathcal{F}$ is known, we propose a bidding algorithm, by using maximum likelihood estimation (MLE) method to achieve at most $widetilde{O}(sqrt{log(d) T})$ regret. Moreover, we generalize this algorithm to the setting with binary feedback and the noise distribution is unknown but belongs to a parametrized family of distributions. For the full information feedback with emph{unknown} noise distribution, we provide an algorithm that achieves regret at most $widetilde{O}(sqrt{dT})$. Our approach combines an estimator for log-concave density functions and then MLE method to learn the noise distribution $mathcal{F}$ and linear weight $alpha_0$ simultaneously. We also provide a lower bound result such that any bidding policy in a broad class must achieve regret at least $Omega(sqrt{T})$, even when the learner receives the full information feedback and $mathcal{F}$ is known.
The physical, black-box hard-label setting is arguably the most realistic threat model for cyber-physical vision systems. In this setting, the attacker only has query access to the model and only receives the top-1 class label without confidence information. Creating small physical stickers that are robust to environmental variation is difficult in the discrete and discontinuous hard-label space because the attack must both design a small shape to perturb within and find robust noise to fill it with. Unfortunately, we find that existing $ell_2$ or $ell_infty$ minimizing hard-label attacks do not easily extend to finding such robust physical perturbation attacks. Thus, we propose GRAPHITE, the first algorithm for hard-label physical attacks on computer vision models. We show that survivability, an estimate of physical variation robustness, can be used in new ways to generate small masks and is a sufficiently smooth function to optimize with gradient-free optimization. We use GRAPHITE to attack a traffic sign classifier and a publicly-available Automatic License Plate Recognition (ALPR) tool using only query access. We evaluate both tools in real-world field tests to measure its physical-world robustness. We successfully cause a Stop sign to be misclassified as a Speed Limit 30 km/hr sign in 95.7% of physical images and cause errors in 75% of physical images for the ALPR tool.
The rapid growth of Decentralized Finance (DeFi) boosts the Ethereum ecosystem. At the same time, attacks towards DeFi applications (apps) are increasing. However, to the best of our knowledge, existing smart contract vulnerability detection tools cannot be directly used to detect DeFi attacks. Thats because they lack the capability to recover and understand high-level DeFi semantics, e.g., a user trades a token pair X and Y in a Decentralized EXchange (DEX). In this work, we focus on the detection of two types of new attacks on DeFi apps, including direct and indirect price manipulation attacks. The former one means that an attacker directly manipulates the token price in DEX by performing an unwanted trade in the same DEX by attacking the vulnerable DeFi app. The latter one means that an attacker indirectly manipulates the token price of the vulnerable DeFi app (e.g., a lending app). To this end, we propose a platform-independent way to recover high-level DeFi semantics by first constructing the cash flow tree from raw Ethereum transactions and then lifting the low-level semantics to high-level ones, including token trade, liquidity mining, and liquidity cancel. Finally, we detect price manipulation attacks using the patterns expressed with the recovered DeFi semantics. We have implemented a prototype named tool{} and applied it to more than 350 million transactions. It successfully detected 432 real-world attacks in the wild. We confirm that they belong to four known security incidents and five zero-day ones. We reported our findings. Two CVEs have been assigned. We further performed an attack analysis to reveal the root cause of the vulnerability, the attack footprint, and the impact of the attack. Our work urges the need to secure the DeFi ecosystem.
Automatically detecting software vulnerabilities in source code is an important problem that has attracted much attention. In particular, deep learning-based vulnerability detectors, or DL-based detectors, are attractive because they do not need human experts to define features or patterns of vulnerabilities. However, such detectors robustness is unclear. In this paper, we initiate the study in this aspect by demonstrating that DL-based detectors are not robust against simple code transformations, dubbed attacks in this paper, as these transformations may be leveraged for malicious purposes. As a first step towards making DL-based detectors robust against such attacks, we propose an innovative framework, dubbed ZigZag, which is centered at (i) decoupling feature learning and classifier learning and (ii) using a ZigZag-style strategy to iteratively refine them until they converge to robust features and robust classifiers. Experimental results show that the ZigZag framework can substantially improve the robustness of DL-based detectors.
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