Search Results for author: Haotian Xue

Found 11 papers, 5 papers with code

Diffusion Policy Attacker: Crafting Adversarial Attacks for Diffusion-based Policies

no code implementations29 May 2024 Yipu Chen, Haotian Xue, Yongxin Chen

We propose DP-Attacker, a suite of algorithms that can craft effective adversarial attacks across all aforementioned scenarios.

RefDrop: Controllable Consistency in Image or Video Generation via Reference Feature Guidance

no code implementations27 May 2024 Jiaojiao Fan, Haotian Xue, Qinsheng Zhang, Yongxin Chen

Motivated by this observation, we find that a rank-1 coefficient is not necessary and simplifies the controllable generation mechanism.

Image Generation Video Generation

Pixel is a Barrier: Diffusion Models Are More Adversarially Robust Than We Think

1 code implementation20 Apr 2024 Haotian Xue, Yongxin Chen

We also find that PDMs can be used as an off-the-shelf purifier to effectively remove the adversarial patterns that were generated on LDMs to protect the images, which means that most protection methods nowadays, to some extent, cannot protect our images from malicious attacks.

Toward effective protection against diffusion based mimicry through score distillation

1 code implementation2 Oct 2023 Haotian Xue, Chumeng Liang, Xiaoyu Wu, Yongxin Chen

In this work, we present novel findings on attacking latent diffusion models (LDM) and propose new plug-and-play strategies for more effective protection.

Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability

1 code implementation NeurIPS 2023 Haotian Xue, Alexandre Araujo, Bin Hu, Yongxin Chen

Neural networks are known to be susceptible to adversarial samples: small variations of natural examples crafted to deliberately mislead the models.

Syntax-guided Localized Self-attention by Constituency Syntactic Distance

1 code implementation21 Oct 2022 Shengyuan Hou, Jushi Kai, Haotian Xue, Bingyu Zhu, Bo Yuan, Longtao Huang, Xinbing Wang, Zhouhan Lin

Recent works have revealed that Transformers are implicitly learning the syntactic information in its lower layers from data, albeit is highly dependent on the quality and scale of the training data.

Machine Translation Translation

Bil-DOS: A Bi-lingual Dialogue Ordering System (for Subway)

1 code implementation11 Oct 2022 Zirong Chen, Haotian Xue

Due to the unfamiliarity to particular words(or proper nouns) for ingredients, non-native English speakers can be extremely confused about the ordering process in restaurants like Subway.

CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks

no code implementations28 Oct 2021 Haotian Xue, Kaixiong Zhou, Tianlong Chen, Kai Guo, Xia Hu, Yi Chang, Xin Wang

In this paper, we investigate GNNs from the lens of weight and feature loss landscapes, i. e., the loss changes with respect to model weights and node features, respectively.


no code implementations29 Sep 2021 Xu Cheng, Xin Wang, Haotian Xue, Zhengyang Liang, Xin Jin, Quanshi Zhang

This paper proposes a hypothesis to analyze the underlying reason for the cognitive difficulty of an image from two perspectives, i. e. a cognitive image usually makes a DNN strongly activated by cognitive concepts; discarding massive non-cognitive concepts may also help the DNN focus on cognitive concepts.

A Hypothesis for the Aesthetic Appreciation in Neural Networks

no code implementations31 Jul 2021 Xu Cheng, Xin Wang, Haotian Xue, Zhengyang Liang, Quanshi Zhang

This paper proposes a hypothesis for the aesthetic appreciation that aesthetic images make a neural network strengthen salient concepts and discard inessential concepts.

Towards a Unified Evaluation of Explanation Methods without Ground Truth

no code implementations20 Nov 2019 Hao Zhang, Jiayi Chen, Haotian Xue, Quanshi Zhang

This paper proposes a set of criteria to evaluate the objectiveness of explanation methods of neural networks, which is crucial for the development of explainable AI, but it also presents significant challenges.

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