arXiv subjects

Wenchang Shi

Publications and source records attributed to Wenchang Shi.

6 recordsLinked to original sources

arXiv2024

A Solution toward Transparent and Practical AI Regulation: Privacy Nutrition Labels for Open-source Generative AI-based Applications

The rapid development and widespread adoption of Generative Artificial Intelligence-based (GAI) applications have greatly enriched our daily lives, benefiting people by enhancing creativity, personalizing experiences, improving accessibility, and fostering innovation and efficiency across various domains. However, along with the development of GAI applications, concerns have been raised about transparency in their privacy practices. Traditional privacy policies often fail to effectively communicate essential privacy information due to their complexity and length, and open-source community developers often neglect privacy practices even more. Only 12.2% of examined open-source GAI apps provide a privacy policy. To address this, we propose a regulation-driven GAI Privacy Label and introduce Repo2Label, a novel framework for automatically generating these labels based on code repositories. Our user study indicates a common endorsement of the proposed GAI privacy label format. Additionally, Repo2Label achieves a precision of 0.81, recall of 0.88, and F1-score of 0.84 based on the benchmark dataset, significantly outperforming the developer self-declared privacy notices. We also discuss the common regulatory (in)compliance of open-source GAI apps, comparison with other privacy notices, and broader impacts to different stakeholders. Our findings suggest that Repo2Label could serve as a significant tool for bolstering the privacy transparency of GAI apps and make them more practical and responsible.

cs.CR↗

arXiv2023

Fight Fire with Fire: Combating Adversarial Patch Attacks using Pattern-randomized Defensive Patches

Object detection has found extensive applications in various tasks, but it is also susceptible to adversarial patch attacks. The ideal defense should be effective, efficient, easy to deploy, and capable of withstanding adaptive attacks. In this paper, we adopt a counterattack strategy to propose a novel and general methodology for defending adversarial attacks. Two types of defensive patches, canary and woodpecker, are specially-crafted and injected into the model input to proactively probe or counteract potential adversarial patches. In this manner, adversarial patch attacks can be effectively detected by simply analyzing the model output, without the need to alter the target model. Moreover, we employ randomized canary and woodpecker injection patterns to defend against defense-aware attacks. The effectiveness and practicality of the proposed method are demonstrated through comprehensive experiments. The results illustrate that canary and woodpecker achieve high performance, even when confronted with unknown attack methods, while incurring limited time overhead. Furthermore, our method also exhibits sufficient robustness against defense-aware attacks, as evidenced by adaptive attack experiments.

cs.CV↗

arXiv2023

BDTS: Blockchain-based Data Trading System

Trading data through blockchain platforms is hard to achieve \textit{fair exchange}. Reasons come from two folds: Firstly, guaranteeing fairness between sellers and consumers is a challenging task as the deception of any participating parties is risk-free. This leads to the second issue where judging the behavior of data executors (such as cloud service providers) among distrustful parties is impractical in the context of traditional trading protocols. To fill the gaps, in this paper, we present a \underline{b}lockchain-based \underline{d}ata \underline{t}rading \underline{s}ystem, named BDTS. BDTS implements a fair-exchange protocol in which benign behaviors can get rewarded while dishonest behaviors will be punished. Our scheme requires the seller to provide consumers with the correct encryption keys for proper execution and encourage a rational data executor to behave faithfully for maximum benefits from rewards. We analyze the strategies of consumers, sellers, and dealers in the trading game and point out that everyone should be honest about their interests so that the game will reach Nash equilibrium. Evaluations prove efficiency and practicability.

cs.CR↗

arXiv2021

SEPAL: Towards a Large-scale Analysis of SEAndroid Policy Customization

To investigate the status quo of SEAndroid policy customization, we propose SEPAL, a universal tool to automatically retrieve and examine the customized policy rules. SEPAL applies the NLP technique and employs and trains a wide&deep model to quickly and precisely predict whether one rule is unregulated or not.Our evaluation shows SEPAL is effective, practical and scalable. We verify SEPAL outperforms the state of the art approach (i.e., EASEAndroid) by 15% accuracy rate on average. In our experiments, SEPAL successfully identifies 7,111 unregulated policy rules with a low false positive rate from 595,236 customized rules (extracted from 774 Android firmware images of 72 manufacturers). We further discover the policy customization problem is getting worse in newer Android versions (e.g., around 8% for Android 7 and nearly 20% for Android 9), even though more and more efforts are made. Then, we conduct a deep study and discuss why the unregulated rules are introduced and how they can compromise user devices. Last, we report some unregulated rules to seven vendors and so far four of them confirm our findings.

cs.CR↗

arXiv2019

Detecting Adversarial Image Examples in Deep Networks with Adaptive Noise Reduction

Recently, many studies have demonstrated deep neural network (DNN) classifiers can be fooled by the adversarial example, which is crafted via introducing some perturbations into an original sample. Accordingly, some powerful defense techniques were proposed. However, existing defense techniques often require modifying the target model or depend on the prior knowledge of attacks. In this paper, we propose a straightforward method for detecting adversarial image examples, which can be directly deployed into unmodified off-the-shelf DNN models. We consider the perturbation to images as a kind of noise and introduce two classic image processing techniques, scalar quantization and smoothing spatial filter, to reduce its effect. The image entropy is employed as a metric to implement an adaptive noise reduction for different kinds of images. Consequently, the adversarial example can be effectively detected by comparing the classification results of a given sample and its denoised version, without referring to any prior knowledge of attacks. More than 20,000 adversarial examples against some state-of-the-art DNN models are used to evaluate the proposed method, which are crafted with different attack techniques. The experiments show that our detection method can achieve a high overall F1 score of 96.39% and certainly raises the bar for defense-aware attacks.

cs.CR↗

arXiv2019

Deep Text Classification Can be Fooled

In this paper, we present an effective method to craft text adversarial samples, revealing one important yet underestimated fact that DNN-based text classifiers are also prone to adversarial sample attack. Specifically, confronted with different adversarial scenarios, the text items that are important for classification are identified by computing the cost gradients of the input (white-box attack) or generating a series of occluded test samples (black-box attack). Based on these items, we design three perturbation strategies, namely insertion, modification, and removal, to generate adversarial samples. The experiment results show that the adversarial samples generated by our method can successfully fool both state-of-the-art character-level and word-level DNN-based text classifiers. The adversarial samples can be perturbed to any desirable classes without compromising their utilities. At the same time, the introduced perturbation is difficult to be perceived.

cs.CR↗