Jian Liu

Jinan Stomatological Hospital

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Ajmal Mian

University of Western Australia

Naveed Akhtar

Hamad Medical Corporation

Zhuohang Li

Vanderbilt University

Yingying Chen

Rutgers, The State University of New Jersey

Cong Shi

East China University of Science and Technology

Bo Yuan

Craft Group (China)

Yi Xie

Fudan University

Jianshu Li

China Automotive Technology and Research Center

Caijuan Shi

North China University of Science and Technology

Shuo Liu

Sichuan International Studies University

Cooperative Institutions

Chinese Academy of Sciences

Zhejiang University

Zhejiang University of Technology

Tsinghua University

University of Chinese Academy of Sciences

National University of Defense Technology

University of Electronic Science and Technology of China

Sun Yat-sen University

Shanghai Jiao Tong University

Harbin Institute of Technology

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Benchmark Data Set and Method for Depth Estimation From Light Field Images

IEEE Transactions on Image Processing (2018)

Mingtao Feng Yaonan Wang Jian Liu Liang Zhang Hasan Firdaus Mohd Zaki

Convolutional Neural Networks (CNN) have performed extremely well for many image analysis tasks. However, supervised training of deep CNN architectures requires huge amounts of labelled data which is unavailable for light field images. In this paper, we leverage on synthetic light field images and propose a two stream CNN network that learns to estimate the disparities of multiple correlated neighbourhood pixels from their Epipolar Plane Images (EPI). Since the EPIs are unrelated except at their intersection, a two stream network is proposed to learn convolution weights individually for the EPIs and then combine the outputs of the two streams for disparity estimation. The CNN estimated disparity map is then refined using the central RGB light field image as a prior in a variational technique. We also propose a new real world dataset comprising light field images of 19 objects captured with the Lytro Illum camera in outdoor scenes and their corresponding 3D pointclouds, as ground truth, captured with the 3dMD scanner. This dataset will be made public to allow more precise 3D pointcloud level comparison of algorithms in the future which is currently not possible. Experiments on the synthetic and real world datasets show that our algorithm outperforms existing state-of-the-art for depth estimation from light field images.

Benchmark (surveying)

Data set

10.1109/tip.2018.2814217

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Citations (61)

Watermark Vaccine: Adversarial Attacks to Prevent Watermark Removal

Lecture notes in computer science (2022)

Xinwei Liu Jian Liu Yang Bai Jindong Gu Tao Chen

10.1007/978-3-031-19781-9_1

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Citations (12)

Variation on correlation immune Boolean and vectorial functions

Advances in Mathematics of Communications (2016)

Jian Liu Sihem Mesnager Lusheng Chen

Correlation immune functions were introduced to protect some shift register based stream ciphers against correlation attacks.For cryptographic applications, relaxing the concept of correlation immunity has been highlighted and proved to be more appropriate in several cryptographic situations.Various weakened notions of correlation immunity and resiliency have been widely introduced for cryptographic functions, but those notions are difficult to handle. As a variation, we focus on the notion of $\varphi$-correlation immunity which is closely related to (fast) correlation attacks on stream ciphers based on nonlinear combiner model. In particular, we exhibit new connections between $\varphi$-correlation immunity and $\epsilon$-almost resiliency, which are two distinct approaches for characterizing relaxed resiliency. We also extend the concept of $\varphi$-correlation immunity introduced by Carlet et al. in 2006 for Boolean functions to vectorial functions and study the main cryptographic parameters of $\varphi$-correlation immune functions. Moreover, we provide new primary constructions of $\varphi$-resilient functions with good designed immunity profile. Specially, we propose a new recursive method to construct $\varphi$-resilient functions with high nonlinearity, high algebraic degree, and monotone increasing immunity profile.

Correlation attack

10.3934/amc.2016048

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Adversarial Attack on Skeleton-Based Human Action Recognition

IEEE Transactions on Neural Networks and Learning Systems (2020)

Jian Liu Naveed Akhtar Ajmal Mian

Deep learning models achieve impressive performance for skeleton-based human action recognition. Graph convolutional networks (GCNs) are particularly suitable for this task due to the graph-structured nature of skeleton data. However, the robustness of these models to adversarial attacks remains largely unexplored due to their complex spatiotemporal nature that must represent sparse and discrete skeleton joints. This work presents the first adversarial attack on skeleton-based action recognition with GCNs. The proposed targeted attack, termed constrained iterative attack for skeleton actions (CIASA), perturbs joint locations in an action sequence such that the resulting adversarial sequence preserves the temporal coherence, spatial integrity, and the anthropomorphic plausibility of the skeletons. CIASA achieves this feat by satisfying multiple physical constraints and employing spatial skeleton realignments for the perturbed skeletons along with regularization of the adversarial skeletons with generative networks. We also explore the possibility of semantically imperceptible localized attacks with CIASA and succeed in fooling the state-of-the-art skeleton action recognition models with high confidence. CIASA perturbations show high transferability in black-box settings. We also show that the perturbed skeleton sequences are able to induce adversarial behavior in the RGB videos created with computer graphics. A comprehensive evaluation with NTU and Kinetics data sets ascertains the effectiveness of CIASA for graph-based skeleton action recognition and reveals the imminent threat to the spatiotemporal deep learning tasks in general.

Skeleton (computer programming)

Action Recognition

Human skeleton

Generative adversarial network

10.1109/tnnls.2020.3043002

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Citations (23)

Viewpoint Invariant Action Recognition using RGB-D Videos

arXiv (Cornell University) (2017)

Jian Liu Naveed Akhtar Ajmal Mian

In video-based action recognition, viewpoint variations often pose major challenges because the same actions can appear different from different views. We use the complementary RGB and Depth information from the RGB-D cameras to address this problem. The proposed technique capitalizes on the spatio-temporal information available in the two data streams to the extract action features that are largely insensitive to the viewpoint variations. We use the RGB data to compute dense trajectories that are translated to viewpoint insensitive deep features under a non-linear knowledge transfer model. Similarly, the Depth stream is used to extract CNN-based view invariant features on which Fourier Temporal Pyramid is computed to incorporate the temporal information. The heterogeneous features from the two streams are combined and used as a dictionary to predict the label of the test samples. To that end, we propose a sparse-dense collaborative representation classification scheme that strikes a balance between the discriminative abilities of the dense and the sparse representations of the samples over the extracted heterogeneous dictionary.

RGB color model

Discriminative model

Action Recognition

Pyramid (geometry)

Representation

10.48550/arxiv.1709.05087

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Citations (3)

Motion feature based video retrieval

Signal Processing (2011)

Jian Liu

Feature (linguistics)

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Enabling Fast and Universal Audio Adversarial Attack Using Generative Model

arXiv (Cornell University) (2020)

Yi Xie Zhuohang Li Cong Shi Jian Liu Yingying Chen

Recently, the vulnerability of DNN-based audio systems to adversarial attacks has obtained the increasing attention. However, the existing audio adversarial attacks allow the adversary to possess the entire user's audio input as well as granting sufficient time budget to generate the adversarial perturbations. These idealized assumptions, however, makes the existing audio adversarial attacks mostly impossible to be launched in a timely fashion in practice (e.g., playing unnoticeable adversarial perturbations along with user's streaming input). To overcome these limitations, in this paper we propose fast audio adversarial perturbation generator (FAPG), which uses generative model to generate adversarial perturbations for the audio input in a single forward pass, thereby drastically improving the perturbation generation speed. Built on the top of FAPG, we further propose universal audio adversarial perturbation generator (UAPG), a scheme crafting universal adversarial perturbation that can be imposed on arbitrary benign audio input to cause misclassification. Extensive experiments show that our proposed FAPG can achieve up to 167X speedup over the state-of-the-art audio adversarial attack methods. Also our proposed UAPG can generate universal adversarial perturbation that achieves much better attack performance than the state-of-the-art solutions.

Source

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Citations (2)

Enabling Fast and Universal Audio Adversarial Attack Using Generative Model

arXiv (Cornell University) (2020)

Yi Xie Zhuohang Li Cong Shi Jian Liu Yingying Chen

10.48550/arxiv.2004.12261

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Citations (5)

Salient object detection in egocentric videos

IET Image Processing (2024)

Hao Zhang Haoran Liang Xing Zhao Jian Liu Ronghua Liang

Abstract In the realm of video salient object detection (VSOD), the majority of research has traditionally been centered on third‐person perspective videos. However, this focus overlooks the unique requirements of certain first‐person tasks, such as autonomous driving or robot vision. To bridge this gap, a novel dataset and a camera‐based VSOD model, CaMSD , specifically designed for egocentric videos, is introduced. First, the SalEgo dataset, comprising 17,400 fully annotated frames for video salient object detection, is presented. Second, a computational model that incorporates a camera movement module is proposed, designed to emulate the patterns observed when humans view videos. Additionally, to achieve precise segmentation of a single salient object during switches between salient objects, as opposed to simultaneously segmenting two objects, a saliency enhancement module based on the Squeeze and Excitation Block is incorporated. Experimental results show that the approach outperforms other state‐of‐the‐art methods in egocentric video salient object detection tasks. Dataset and codes can be found at https://github.com/hzhang1999/SalEgo .

10.1049/ipr2.13080

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Citations (1)

"Adversarial Examples" for Proof-of-Learning

arXiv (Cornell University) (2021)

Rui Zhang Jian Liu Yuan Ding Zhibo Wu Qingbiao Wang

In S&P '21, Jia et al. proposed a new concept/mechanism named proof-of-learning (PoL), which allows a prover to demonstrate ownership of a machine learning model by proving integrity of the training procedure. It guarantees that an adversary cannot construct a valid proof with less cost (in both computation and storage) than that made by the prover in generating the proof. A PoL proof includes a set of intermediate models recorded during training, together with the corresponding data points used to obtain each recorded model. 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. In this paper, however, we show that PoL is vulnerable to ``adversarial examples''! Specifically, in a similar way as optimizing an adversarial example, we could make an arbitrarily-chosen data point ``generate'' a given model, hence efficiently generating intermediate models with correct data points. We demonstrate, both theoretically and empirically, that we are able to generate a valid proof with significantly less cost than generating a proof by the prover.

Gas meter prover

Proof of concept

10.48550/arxiv.2108.09454

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Citations (2)