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Xiaodan Li

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7 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

Comprehensive Assessment and Analysis for NSFW Content Erasure in Text-to-Image Diffusion models

Die Chen
Zhiwen Li
Cen Chen
Yuexiang Xie
Xiaodan Li
Jinyan Ye
Yingda Chen
Yaliang Li

Text-to-image diffusion models have gained widespread application across various domains, demonstrating remarkable creative potential. However, the strong generalization capabilities of diffusion models can inadvertently lead to the generation of not-safe-for-work (NSFW) content, posing significant risks to their safe deployment. While several concept erasure methods have been proposed to mitigate the issue associated with NSFW content, a comprehensive evaluation of their effectiveness across various scenarios remains absent. To bridge this gap, we introduce a full-pipeline toolkit specifically designed for concept erasure and conduct the first systematic study of NSFW concept erasure methods. By examining the interplay between the underlying mechanisms and empirical observations, we provide in-depth insights and practical guidance for the effective application of concept erasure methods in various real-world scenarios, with the aim of advancing the understanding of content safety in diffusion models and establishing a solid foundation for future research and development in this critical area.

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ICLR Conference 2024 Conference Paper

Boosting Vanilla Lightweight Vision Transformers via Re-parameterization

Zhentao Tan
Xiaodan Li
Yue Wu
Qi Chu 0001
Le Lu 0001
Nenghai Yu
Jieping Ye

Large-scale Vision Transformers have achieved promising performance on downstream tasks through feature pre-training. However, the performance of vanilla lightweight Vision Transformers (ViTs) is still far from satisfactory compared to that of recent lightweight CNNs or hybrid networks. In this paper, we aim to unlock the potential of vanilla lightweight ViTs by exploring the adaptation of the widely-used re-parameterization technology to ViTs for improving learning ability during training without increasing the inference cost. The main challenge comes from the fact that CNNs perfectly complement with re-parameterization over convolution and batch normalization, while vanilla Transformer architectures are mainly comprised of linear and layer normalization layers. We propose to incorporate the nonlinear ensemble into linear layers by expanding the depth of the linear layers with batch normalization and fusing multiple linear features with hierarchical representation ability through a pyramid structure. We also discover and solve a new transformer-specific distribution rectification problem caused by multi-branch re-parameterization. Finally, we propose our Two-Dimensional Re-parameterized Linear module (TDRL) for ViTs. Under the popular self-supervised pre-training and supervised fine-tuning strategy, our TDRL can be used in these two stages to enhance both generic and task-specific representation. Experiments demonstrate that our proposed method not only boosts the performance of vanilla Vit-Tiny on various vision tasks to new state-of-the-art (SOTA) but also shows promising generality ability on other networks. Code will be available.

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NeurIPS Conference 2024 Conference Paper

Transferability Bound Theory: Exploring Relationship between Adversarial Transferability and Flatness

Mingyuan Fan
Xiaodan Li
Cen Chen
Wenmeng Zhou
Yaliang Li

A prevailing belief in attack and defense community is that the higher flatness of adversarial examples enables their better cross-model transferability, leading to a growing interest in employing sharpness-aware minimization and its variants. However, the theoretical relationship between the transferability of adversarial examples and their flatness has not been well established, making the belief questionable. To bridge this gap, we embark on a theoretical investigation and, for the first time, derive a theoretical bound for the transferability of adversarial examples with few practical assumptions. Our analysis challenges this belief by demonstrating that the increased flatness of adversarial examples does not necessarily guarantee improved transferability. Moreover, building upon the theoretical analysis, we propose TPA, a Theoretically Provable Attack that optimizes a surrogate of the derived bound to craft adversarial examples. Extensive experiments across widely used benchmark datasets and various real-world applications show that TPA can craft more transferable adversarial examples compared to state-of-the-art baselines. We hope that these results can recalibrate preconceived impressions within the community and facilitate the development of stronger adversarial attack and defense mechanisms.

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JBHI Journal 2022 Journal Article

BDBB: A Novel Beta-Distribution-Based Biclustering Algorithm for Revealing Local Co-Methylation Patterns in Epi-Transcriptome Profiling Data

Zhaoyang Liu
Yuteng Xiao
Hongsheng Yin
Xiaodan Li
Shutao Chen
Kaijian Xia
Lin Zhang

N6-methyladenosine (m 6 A) has been shown to play crucial roles in RNA metabolism, physiology, and pathological processes. However, the specific regulatory mechanisms of most methylation sites remain uncharted due to the complexity of life processes. Biological experimental methods are costly to solve this problem, and computational methods are relatively lacking. The discovery of local co-methylation patterns (LCPs) of m 6 A epi-transcriptome data can benefit to solve the above problems. Based on this, we propose a novel biclustering algorithm based on the beta distribution (BDBB), which realizes the mining of LCPs of m 6 A epi-transcriptome data. BDBB employs the Gibbs sampling method to complete parameter estimation. In the process of modeling, LCPs are recognized as sharp beta distributions compared to the background distribution. Simulation study showed BDBB can extract all the three actual LCPs implanted in the background data and the overlap conditions between them with considerable accuracy (almost close to 100%). On MeRIP-Seq data of 69, 446 methylation sites under 32 experimental conditions from 10 human cell lines, BDBB unveiled two LCPs, and Gene Ontology (GO) enrichment analysis showed that they were enriched in histone modification and embryo development, etc. important biological processes respectively. The GOE_Score scoring indicated that the biclustering results of BDBB in the m 6 A epi-transcriptome data are more biologically meaningful than the results of other biclustering algorithms.

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ICLR Conference 2022 Conference Paper

Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains

Qilong Zhang
Xiaodan Li
Yuefeng Chen
Jingkuan Song
Lianli Gao
Yuan He 0011
Hui Xue 0001

Adversarial examples have posed a severe threat to deep neural networks due to their transferable nature. Currently, various works have paid great efforts to enhance the cross-model transferability, which mostly assume the substitute model is trained in the same domain as the target model. However, in reality, the relevant information of the deployed model is unlikely to leak. Hence, it is vital to build a more practical black-box threat model to overcome this limitation and evaluate the vulnerability of deployed models. In this paper, with only the knowledge of the ImageNet domain, we propose a Beyond ImageNet Attack (BIA) to investigate the transferability towards black-box domains (unknown classification tasks). Specifically, we leverage a generative model to learn the adversarial function for disrupting low-level features of input images. Based on this framework, we further propose two variants to narrow the gap between the source and target domains from the data and model perspectives, respectively. Extensive experiments on coarse-grained and fine-grained domains demonstrate the effectiveness of our proposed methods. Notably, our methods outperform state-of-the-art approaches by up to 7.71\% (towards coarse-grained domains) and 25.91\% (towards fine-grained domains) on average. Our code is available at \url{https://github.com/Alibaba-AAIG/Beyond-ImageNet-Attack}.

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NeurIPS Conference 2022 Conference Paper

Boosting Out-of-distribution Detection with Typical Features

Yao Zhu
Yuefeng Chen
Chuanlong Xie
Xiaodan Li
Rong Zhang
Hui Xue'
Xiang Tian
Bolun Zheng

Out-of-distribution (OOD) detection is a critical task for ensuring the reliability and safety of deep neural networks in real-world scenarios. Different from most previous OOD detection methods that focus on designing OOD scores or introducing diverse outlier examples to retrain the model, we delve into the obstacle factors in OOD detection from the perspective of typicality and regard the feature's high-probability region of the deep model as the feature's typical set. We propose to rectify the feature into its typical set and calculate the OOD score with the typical features to achieve reliable uncertainty estimation. The feature rectification can be conducted as a plug-and-play module with various OOD scores. We evaluate the superiority of our method on both the commonly used benchmark (CIFAR) and the more challenging high-resolution benchmark with large label space (ImageNet). Notably, our approach outperforms state-of-the-art methods by up to 5. 11% in the average FPR95 on the ImageNet benchmark.

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NeurIPS Conference 2022 Conference Paper

Enhance the Visual Representation via Discrete Adversarial Training

Xiaofeng Mao
Yuefeng Chen
Ranjie Duan
Yao Zhu
Gege Qi
Shaokai Ye
Xiaodan Li
Rong Zhang

Adversarial Training (AT), which is commonly accepted as one of the most effective approaches defending against adversarial examples, can largely harm the standard performance, thus has limited usefulness on industrial-scale production and applications. Surprisingly, this phenomenon is totally opposite in Natural Language Processing (NLP) task, where AT can even benefit for generalization. We notice the merit of AT in NLP tasks could derive from the discrete and symbolic input space. For borrowing the advantage from NLP-style AT, we propose Discrete Adversarial Training (DAT). DAT leverages VQGAN to reform the image data to discrete text-like inputs, i. e. visual words. Then it minimizes the maximal risk on such discrete images with symbolic adversarial perturbations. We further give an explanation from the perspective of distribution to demonstrate the effectiveness of DAT. As a plug-and-play technique for enhancing the visual representation, DAT achieves significant improvement on multiple tasks including image classification, object detection and self-supervised learning. Especially, the model pre-trained with Masked Auto-Encoding (MAE) and fine-tuned by our DAT without extra data can get 31. 40 mCE on ImageNet-C and 32. 77% top-1 accuracy on Stylized-ImageNet, building the new state-of-the-art. The code will be available at https: //github. com/alibaba/easyrobust.

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