Search Results for author: Chun Pong Lau

Found 18 papers, 3 papers with code

Instruct2Attack: Language-Guided Semantic Adversarial Attacks

no code implementations27 Nov 2023 Jiang Liu, Chen Wei, Yuxiang Guo, Heng Yu, Alan Yuille, Soheil Feizi, Chun Pong Lau, Rama Chellappa

We propose Instruct2Attack (I2A), a language-guided semantic attack that generates semantically meaningful perturbations according to free-form language instructions.

Whole-body Detection, Recognition and Identification at Altitude and Range

no code implementations9 Nov 2023 Siyuan Huang, Ram Prabhakar Kathirvel, Chun Pong Lau, Rama Chellappa

In this paper, we address the challenging task of whole-body biometric detection, recognition, and identification at distances of up to 500m and large pitch angles of up to 50 degree.

Body Detection TAR

GADER: GAit DEtection and Recognition in the Wild

no code implementations27 Jul 2023 Yuxiang Guo, Cheng Peng, Ram Prabhakar, Chun Pong Lau, Rama Chellappa

Gait recognition holds the promise of robustly identifying subjects based on their walking patterns instead of color information.

Gait Recognition

DiffProtect: Generate Adversarial Examples with Diffusion Models for Facial Privacy Protection

1 code implementation23 May 2023 Jiang Liu, Chun Pong Lau, Rama Chellappa

In this work, we ask: can diffusion models be used to generate adversarial examples to improve both visual quality and attack performance?

Image Generation

Attribute-Guided Encryption with Facial Texture Masking

no code implementations22 May 2023 Chun Pong Lau, Jiang Liu, Rama Chellappa

In this paper, we propose Attribute Guided Encryption with Facial Texture Masking (AGE-FTM) that performs a dual manifold adversarial attack on FR systems to achieve both good visual quality and high black box attack success rates.

Adversarial Attack Attribute +1

Multi-Modal Human Authentication Using Silhouettes, Gait and RGB

no code implementations8 Oct 2022 Yuxiang Guo, Cheng Peng, Chun Pong Lau, Rama Chellappa

In this work, we propose Dual-Modal Ensemble (DME), which combines both RGB and silhouette data to achieve more robust performances for indoor and outdoor whole-body based recognition.

Gait Recognition

Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection

1 code implementation CVPR 2022 Jiang Liu, Alexander Levine, Chun Pong Lau, Rama Chellappa, Soheil Feizi

In addition, we design a robust shape completion algorithm, which is guaranteed to remove the entire patch from the images if the outputs of the patch segmenter are within a certain Hamming distance of the ground-truth patch masks.

Adversarial Attack Detection Adversarial Defense +5

Identification of Attack-Specific Signatures in Adversarial Examples

no code implementations13 Oct 2021 Hossein Souri, Pirazh Khorramshahi, Chun Pong Lau, Micah Goldblum, Rama Chellappa

The adversarial attack literature contains a myriad of algorithms for crafting perturbations which yield pathological behavior in neural networks.

Adversarial Attack

Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks

no code implementations NeurIPS 2020 Wei-An Lin, Chun Pong Lau, Alexander Levine, Rama Chellappa, Soheil Feizi

Using OM-ImageNet, we first show that adversarial training in the latent space of images improves both standard accuracy and robustness to on-manifold attacks.

Adversarial Robustness

ATFaceGAN: Single Face Image Restoration and Recognition from Atmospheric Turbulence

no code implementations7 Oct 2019 Chun Pong Lau, Hossein Souri, Rama Chellappa

To mitigate the degradation due to turbulence which includes deformation and blur, we propose a generative single frame restoration algorithm which disentangles the blur and deformation due to turbulence and reconstructs a restored image.

Disentanglement Face Recognition +1

Subsampled Turbulence Removal Network

no code implementations12 Jul 2018 Wai Ho Chak, Chun Pong Lau, Lok Ming Lui

Instead of requiring a massive training sample size in deep networks, we purpose a training strategy that is based on a new data augmentation method to model turbulence from a relatively small dataset.

Data Augmentation

Variational models for joint subsampling and reconstruction of turbulence-degraded images

no code implementations8 Dec 2017 Chun Pong Lau, Yu Hin Lai, Lok Ming Lui

The energy consists of a fidelity term measuring the discrepancy between the extracted image and the subsampled frames, as well as regularization terms on the extracted image and the subsample.

Image retargeting via Beltrami representation

no code implementations11 Oct 2017 Chun Pong Lau, Chun Pang Yung, Lok Ming Lui

In this paper, we propose a simple and yet effective method to resize an image, which preserves the geometry of the important content, using the Beltrami representation.

Image Retargeting

Restoration of Atmospheric Turbulence-distorted Images via RPCA and Quasiconformal Maps

no code implementations11 Apr 2017 Chun Pong Lau, Yu Hin Lai, Lok Ming Lui

The subsampled image sequence is then stabilized by applying the Robust Principal Component Analysis (RPCA) on the deformation fields between image frames and warping the image frames by a quasiconformal map associated with the low-rank part of the deformation matrix.

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