477 citations · 972 across the 72 of their papers we have counts for
13 papers · 1 filter
Context-Aware Transfer Attacks for Object Detection
Zikui Cai, Xinxin Xie, Shasha Li +5
Blackbox transfer attacks for image classifiers have been extensively studied in recent years. In contrast, little progress has been made on transfer attacks for object detectors.…
Adversarial Attacks on Black Box Video Classifiers: Leveraging the Power of Geometric Transformations
Shasha Li, Abhishek Aich, Shitong Zhu +4
When compared to the image classification models, black-box adversarial attacks against video classification models have been largely understudied. This could be possible because,…
ADC: Adversarial attacks against object Detection that evade Context consistency checks
Mingjun Yin, Shasha Li, Chengyu Song +3
Deep Neural Networks (DNNs) have been shown to be vulnerable to adversarial examples, which are slightly perturbed input images which lead DNNs to make wrong predictions. To protec…
Exploiting Multi-Object Relationships for Detecting Adversarial Attacks in Complex Scenes
Mingjun Yin, Shasha Li, Zikui Cai +4
Vision systems that deploy Deep Neural Networks (DNNs) are known to be vulnerable to adversarial examples. Recent research has shown that checking the intrinsic consistencies in th…
Spatio-Temporal Representation Factorization for Video-based Person Re-Identification
Abhishek Aich, Meng Zheng, Srikrishna Karanam +3
Despite much recent progress in video-based person re-identification (re-ID), the current state-of-the-art still suffers from common real-world challenges such as appearance simila…
Ada-VSR: Adaptive Video Super-Resolution with Meta-Learning
Akash Gupta, Padmaja Jonnalagedda, Bir Bhanu +1
Most of the existing works in supervised spatio-temporal video super-resolution (STVSR) heavily rely on a large-scale external dataset consisting of paired low-resolution low-frame…
Multi-Expert Adversarial Attack Detection in Person Re-identification Using Context Inconsistency
Xueping Wang, Shasha Li, Min Liu +2
The success of deep neural networks (DNNs) has promoted the widespread applications of person re-identification (ReID). However, ReID systems inherit the vulnerability of DNNs to m…
Reconstruction guided Meta-learning for Few Shot Open Set Recognition
Sayak Nag, Dripta S. Raychaudhuri, Sujoy Paul +1
In many applications, we are constrained to learn classifiers from very limited data (few-shot classification). The task becomes even more challenging if it is also required to ide…
Deep Quantized Representation for Enhanced Reconstruction
Akash Gupta, Abhishek Aich, Kevin Rodriguez +2
While machine learning approaches have shown remarkable performance in biomedical image analysis, most of these methods rely on high-quality and accurate imaging data. However, col…
Cross-domain Imitation from Observations
Dripta S. Raychaudhuri, Sujoy Paul, Jeroen van Baar +1
Imitation learning seeks to circumvent the difficulty in designing proper reward functions for training agents by utilizing expert behavior. With environments modeled as Markov Dec…
Unsupervised Multi-source Domain Adaptation Without Access to Source Data
Sk Miraj Ahmed, Dripta S. Raychaudhuri, Sujoy Paul +2
Unsupervised Domain Adaptation (UDA) aims to learn a predictor model for an unlabeled domain by transferring knowledge from a separate labeled source domain. However, most of these…
Detection and Localization of Facial Expression Manipulations
Ghazal Mazaheri, Amit K. Roy-Chowdhury
Concern regarding the wide-spread use of fraudulent images/videos in social media necessitates precise detection of such fraud. The importance of facial expressions in communicatio…
Learning to identify image manipulations in scientific publications
Ghazal Mazaheri, Kevin Urrutia Avila, Amit K. Roy-Chowdhury
Adherence to scientific community standards ensures objectivity, clarity, reproducibility, and helps prevent bias, fabrication, falsification, and plagiarism. To help scientific in…