no code implementations • CVPR 2023 • Zhibo Wang, He Wang, Shuaifan Jin, Wenwen Zhang, Jiahui Hu, Yan Wang, Peng Sun, Wei Yuan, Kaixin Liu, Kui Ren
In this paper, we propose an adversarial features-based face privacy protection (AdvFace) approach to generate privacy-preserving adversarial features, which can disrupt the mapping from adversarial features to facial images to defend against reconstruction attacks.
1 code implementation • CVPR 2023 • Zhou Yu, Lixiang Zheng, Zhou Zhao, Fei Wu, Jianping Fan, Kui Ren, Jun Yu
A recent benchmark AGQA poses a promising paradigm to generate QA pairs automatically from pre-annotated scene graphs, enabling it to measure diverse reasoning abilities with granular control.
no code implementations • 13 Apr 2023 • Jian Liu, Rui Zhang, Sebastian Szyller, Kui Ren, N. Asokan
Our core idea is that a malicious accuser can deviate (without detection) from the specified MOR process by finding (transferable) adversarial examples that successfully serve as evidence against independent suspect models.
no code implementations • 27 Feb 2023 • Buyu Liu, BaoJun, Jianping Fan, Xi Peng, Kui Ren, Jun Yu
More desired attacks, to this end, should be able to fool defenses with such consistency checks.
no code implementations • 8 Feb 2023 • Björn Engquist, Kui Ren, Yunan Yang
This paper develops and analyzes a stochastic derivative-free optimization strategy.
no code implementations • CVPR 2023 • Zhibo Wang, Hongshan Yang, Yunhe Feng, Peng Sun, Hengchang Guo, Zhifei Zhang, Kui Ren
In this paper, we propose the Transferable Targeted Adversarial Attack (TTAA), which can capture the distribution information of the target class from both label-wise and feature-wise perspectives, to generate highly transferable targeted adversarial examples.
no code implementations • 1 Dec 2022 • Ziqi Yang, Lijin Wang, Da Yang, Jie Wan, Ziming Zhao, Ee-Chien Chang, Fan Zhang, Kui Ren
Besides, our further experiments show that PURIFIER is also effective in defending adversarial model inversion attacks and attribute inference attacks.
1 code implementation • 18 Nov 2022 • Jiachen Lei, Shuang Ma, Zhongjie Ba, Sai Vemprala, Ashish Kapoor, Kui Ren
In this report, we present our approach and empirical results of applying masked autoencoders in two egocentric video understanding tasks, namely, Object State Change Classification and PNR Temporal Localization, of Ego4D Challenge 2022.
no code implementations • 14 Nov 2022 • Shuo Shao, Wenyuan Yang, Hanlin Gu, Jian Lou, Zhan Qin, Lixin Fan, Qiang Yang, Kui Ren
Copyright protection of the Federated Learning (FL) model has become a major concern since malicious clients in FL can stealthily distribute or sell the FL model to other parties.
no code implementations • 10 Nov 2022 • Meng Chen, Li Lu, Jiadi Yu, Yingying Chen, Zhongjie Ba, Feng Lin, Kui Ren
In this paper, we propose a voice de-identification system, which uses adversarial examples to balance the privacy and utility of voice services.
no code implementations • 17 Oct 2022 • Kui Ren, Lu Zhang
The task of simultaneously reconstructing multiple physical coefficients in partial differential equations from observed data is ubiquitous in applications.
1 code implementation • 4 Oct 2022 • Xiaochen Li, Yuke Hu, Weiran Liu, Hanwen Feng, Li Peng, Yuan Hong, Kui Ren, Zhan Qin
Although the solution based on Local Differential Privacy (LDP) addresses the above problems, it leads to the low accuracy of the trained model.
no code implementations • 17 Jul 2022 • Shaoyu Dou, Kai Yang, Yang Jiao, Chengbo Qiu, Kui Ren
The proposed framework aspires to offer a stepping stone that gives rise to a systematic approach to model and learn similarities among a multitude of event-triggered time series.
no code implementations • 5 Jun 2022 • Guodong Cao, Zhibo Wang, Xiaowei Dong, Zhifei Zhang, Hengchang Guo, Zhan Qin, Kui Ren
However, most existing works are still trapped in the dilemma between higher accuracy and stronger robustness since they tend to fit a model towards robust features (not easily tampered with by adversaries) while ignoring those non-robust but highly predictive features.
no code implementations • 12 Apr 2022 • Björn Engquist, Kui Ren, Yunan Yang
With this, we prove the global convergence of the algorithm with an algebraic rate both in probability and in the parameter space.
no code implementations • CVPR 2022 • Zhibo Wang, Xiaowei Dong, Henry Xue, Zhifei Zhang, Weifeng Chiu, Tao Wei, Kui Ren
Prioritizing fairness is of central importance in artificial intelligence (AI) systems, especially for those societal applications, e. g., hiring systems should recommend applicants equally from different demographic groups, and risk assessment systems must eliminate racism in criminal justice.
2 code implementations • ICLR 2022 • Kunzhe Huang, Yiming Li, Baoyuan Wu, Zhan Qin, Kui Ren
Recent studies have revealed that deep neural networks (DNNs) are vulnerable to backdoor attacks, where attackers embed hidden backdoors in the DNN model by poisoning a few training samples.
no code implementations • ICLR 2022 • Björn Engquist, Kui Ren, Yunan Yang
The generalization capacity of various machine learning models exhibits different phenomena in the under- and over-parameterized regimes.
1 code implementation • 21 Aug 2021 • Rui Zhang, Jian Liu, Yuan Ding, Zhibo Wu, Qingbiao Wang, Kui Ren
Jia et al. claimed that an adversary merely knowing the final model and training dataset cannot efficiently find a set of intermediate models with correct data points.
1 code implementation • ICCV 2021 • Zhibo Wang, Hengchang Guo, Zhifei Zhang, Wenxin Liu, Zhan Qin, Kui Ren
More specifically, we obtain feature importance by introducing the aggregate gradient, which averages the gradients with respect to feature maps of the source model, computed on a batch of random transforms of the original clean image.
no code implementations • 11 Oct 2020 • Weilin Li, Kui Ren, Donsub Rim
The range characterization is obtained by first showing that the ADRT is a bijection between images supported on infinite half-strips, then identifying the linear subspaces that stay finitely supported under the inversion formula.
no code implementations • 12 Jun 2020 • Sekhar Rajendran, Zhi Sun, Feng Lin, Kui Ren
Our proposed solution, Metasurface RF-Fingerprinting Injection (MeRFFI), is to inject a carefully-designed radio frequency fingerprint into the wireless physical layer that can increase the security of a stationary IoT device with minimal overhead.
no code implementations • 14 May 2020 • Tianhang Zheng, Sheng Liu, Changyou Chen, Junsong Yuan, Baochun Li, Kui Ren
We first formulate generation of adversarial skeleton actions as a constrained optimization problem by representing or approximating the physiological and physical constraints with mathematical formulations.
no code implementations • 8 Apr 2020 • Jianwei Liu, Jinsong Han, Feng Lin, Kui Ren
Wireless signal-based gesture recognition has promoted the developments of VR game, smart home, etc.
no code implementations • 24 Mar 2020 • Yang Liu, Zhuo Ma, Ximeng Liu, Jian Liu, Zhongyuan Jiang, Jianfeng Ma, Philip Yu, Kui Ren
To this end, machine unlearning becomes a popular research topic, which allows users to eliminate memorization of their private data from a trained machine learning model. In this paper, we propose the first uniform metric called for-getting rate to measure the effectiveness of a machine unlearning method.
no code implementations • 15 Nov 2019 • Bjorn Engquist, Kui Ren, Yunan Yang
This work characterizes, analytically and numerically, two major effects of the quadratic Wasserstein ($W_2$) distance as the measure of data discrepancy in computational solutions of inverse problems.
no code implementations • 26 Apr 2019 • Hengtong Zhang, Tianhang Zheng, Jing Gao, Chenglin Miao, Lu Su, Yaliang Li, Kui Ren
Knowledge graph embedding (KGE) is a technique for learning continuous embeddings for entities and relations in the knowledge graph. Due to its benefit to a variety of downstream tasks such as knowledge graph completion, question answering and recommendation, KGE has gained significant attention recently.
3 code implementations • ICCV 2019 • Tianhang Zheng, Changyou Chen, Junsong Yuan, Bo Li, Kui Ren
Our motivation for constructing a saliency map is by point dropping, which is a non-differentiable operator.
no code implementations • 10 Oct 2018 • Tianhang Zheng, Changyou Chen, Kui Ren
In this paper, we give a negative answer by proposing a training paradigm that is comparable to PGD adversarial training on several standard datasets, while only using noisy-natural samples.
no code implementations • 10 Oct 2018 • Yaliang Li, Houping Xiao, Zhan Qin, Chenglin Miao, Lu Su, Jing Gao, Kui Ren, Bolin Ding
To better utilize sensory data, the problem of truth discovery, whose goal is to estimate user quality and infer reliable aggregated results through quality-aware data aggregation, has emerged as a hot topic.
no code implementations • 6 Oct 2018 • Fei Wang, Jinsong Han, Feng Lin, Kui Ren
Wi-Fi signals-based person identification attracts increasing attention in the booming Internet-of-Things era mainly due to its pervasiveness and passiveness.
4 code implementations • 16 Aug 2018 • Tianhang Zheng, Changyou Chen, Kui Ren
Recent work on adversarial attack has shown that Projected Gradient Descent (PGD) Adversary is a universal first-order adversary, and the classifier adversarially trained by PGD is robust against a wide range of first-order attacks.
no code implementations • 10 Aug 2018 • Xiao Chen, Chaoran Li, Derui Wang, Sheng Wen, Jun Zhang, Surya Nepal, Yang Xiang, Kui Ren
In contrast to existing works, the adversarial examples crafted by our method can also deceive recent machine learning based detectors that rely on semantic features such as control-flow-graph.
Cryptography and Security