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Pengxu Wei

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8 papers

2 author rows

ICML Conference 2025 Conference Paper

Are High-Quality AI-Generated Images More Difficult for Models to Detect?

Yao Xiao
Binbin Yang
Weiyan Chen
Jiahao Chen
Zijie Cao
ZiYi Dong
Xiangyang Ji
Liang Lin

The remarkable evolution of generative models has enabled the generation of high-quality, visually attractive images, often perceptually indistinguishable from real photographs to human eyes. This has spurred significant attention on AI-generated image (AIGI) detection. Intuitively, higher image quality should increase detection difficulty. However, our systematic study on cutting-edge text-to-image generators reveals a counterintuitive finding: AIGIs with higher quality scores, as assessed by human preference models, tend to be more easily detected by existing models. To investigate this, we examine how the text prompts for generation and image characteristics influence both quality scores and detector accuracy. We observe that images from short prompts tend to achieve higher preference scores while being easier to detect. Furthermore, through clustering and regression analyses, we verify that image characteristics like saturation, contrast, and texture richness collectively impact both image quality and detector accuracy. Finally, we demonstrate that the performance of off-the-shelf detectors can be enhanced across diverse generators and datasets by selecting input patches based on the predicted scores of our regression models, thus substantiating the broader applicability of our findings. Code and data are available at https: //github. com/Coxy7/AIGI-Detection-Quality-Paradox.

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NeurIPS Conference 2025 Conference Paper

Delving into Cascaded Instability: A Lipschitz Continuity View on Image Restoration and Object Detection Synergy

Qing Zhao
Weijian Deng
Pengxu Wei
ZiYi Dong
Hannan Lu
Xiangyang Ji
Liang Lin

To improve detection robustness in adverse conditions (e. g. , haze and low light), image restoration is commonly applied as a pre-processing step to enhance image quality for the detector. However, the functional mismatch between restoration and detection networks can introduce instability and hinder effective integration---an issue that remains underexplored. We revisit this limitation through the lens of Lipschitz continuity, analyzing the functional differences between restoration and detection networks in both the input space and the parameter space. Our analysis shows that restoration networks perform smooth, continuous transformations, while object detectors operate with discontinuous decision boundaries, making them highly sensitive to minor perturbations. This mismatch introduces instability in traditional cascade frameworks, where even imperceptible noise from restoration is amplified during detection, disrupting gradient flow and hindering optimization. To address this, we propose Lipschitz-regularized object detection (LROD), a simple yet effective framework that integrates image restoration directly into the detector’s feature learning, harmonizing the Lipschitz continuity of both tasks during training. We implement this framework as Lipschitz-regularized YOLO (LR-YOLO), extending seamlessly to existing YOLO detectors. Extensive experiments on haze and low-light benchmarks demonstrate that LR-YOLO consistently improves detection stability, optimization smoothness, and overall accuracy.

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AAAI Conference 2025 Conference Paper

DigitalLLaVA: Incorporating Digital Cognition Capability for Physical World Comprehension in Multimodal LLMs

Shiyu Li
Pengxu Wei
Pengchong Qiao
Chang Liu
Jie Chen

Multimodal Large Language Models (MLLMs) have shown remarkable cognitive capabilities in various cross-modal tasks.However, existing MLLMs struggle with tasks that require physical digital cognition, such as accurately reading an electric meter or pressure gauge. This limitation significantly reduces their effectiveness in practical applications like industrial monitoring and home energy management, where digital sensors are not feasible. For humans, physical digits are artificially defined quantities presented on specific carriers, which require training to recognize. As existing MLLMs are only pre-trained in the manner of object recognition, they fail to comprehend the relationship between digital carriers and their reading. To this end, referring to human behavior, we propose a novel DigitalLLaVA method to explicitly inject digital cognitive abilities into MLLMs in a two-step manner. In the first step, to improve the MLLM's understanding of physical digit carriers, we propose a digit carrier mapping method. This step utilizes object-level text-image pairs to enhance the model's comprehension of objects containing physical digits. For the second step, unlike previous methods that rely on sequential digital prediction or digit regression, we propose a 32 bit floating point simulation approach that treats digit prediction as a whole. Using digit-level text-image pairs, we train three float heads to predict 32-bit floating-point numbers using 0/1 binary classification. This step significantly reduces the search space, making the prediction process more robust and straightforward. Being simple but effective, our method can identify very precise metrics (i.e., accurate to ±0.001) and provide floating-point results, showing its applicability in digital carrier domains.

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ICLR Conference 2025 Conference Paper

Towards Understanding the Robustness of Diffusion-Based Purification: A Stochastic Perspective

Yiming Liu
Kezhao Liu
Yao Xiao
Ziyi Dong
Xiaogang Xu 0002
Pengxu Wei
Liang Lin

Diffusion-Based Purification (DBP) has emerged as an effective defense mechanism against adversarial attacks. The success of DBP is often attributed to the forward diffusion process, which reduces the distribution gap between clean and adversarial images by adding Gaussian noise. Although this explanation is theoretically grounded, the precise contribution of this process to robustness remains unclear. In this paper, through a systematic investigation, we propose that the intrinsic stochasticity in the DBP procedure is the primary factor driving robustness. To explore this hypothesis, we introduce a novel Deterministic White-Box (DW-box) evaluation protocol to assess robustness in the absence of stochasticity, and analyze attack trajectories and loss landscapes. Our results suggest that DBP models primarily leverage stochasticity to evade effective attack directions, and that their ability to purify adversarial perturbations can be weak. To further enhance the robustness of DBP models, we propose Adversarial Denoising Diffusion Training (ADDT), which incorporates classifier-guided adversarial perturbations into diffusion training, thereby strengthening the models' ability to purify adversarial perturbations. Additionally, we propose Rank-Based Gaussian Mapping (RBGM) to improve the compatibility of perturbations with diffusion models. Experimental results validate the effectiveness of ADDT. In conclusion, our study suggests that future research on DBP can benefit from the perspective of decoupling stochasticity-based and purification-based robustness.

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ICML Conference 2024 Conference Paper

Image Restoration Through Generalized Ornstein-Uhlenbeck Bridge

Conghan Yue
Zhengwei Peng
Junlong Ma
Shiyan Du
Pengxu Wei
Dongyu Zhang 0002

Diffusion models exhibit powerful generative capabilities enabling noise mapping to data via reverse stochastic differential equations. However, in image restoration, the focus is on the mapping relationship from low-quality to high-quality images. Regarding this issue, we introduce the Generalized Ornstein-Uhlenbeck Bridge (GOUB) model. By leveraging the natural mean-reverting property of the generalized OU process and further eliminating the variance of its steady-state distribution through the Doob’s h –transform, we achieve diffusion mappings from point to point enabling the recovery of high-quality images from low-quality ones. Moreover, we unravel the fundamental mathematical essence shared by various bridge models, all of which are special instances of GOUB and empirically demonstrate the optimality of our proposed models. Additionally, we present the corresponding Mean-ODE model adept at capturing both pixel-level details and structural perceptions. Experimental outcomes showcase the state-of-the-art performance achieved by both models across diverse tasks, including inpainting, deraining, and super-resolution. Code is available at https: //github. com/Hammour-steak/GOUB.

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ICML Conference 2024 Conference Paper

Kepler codebook

Junrong Lian
Ziyue Dong
Pengxu Wei
Wei Ke 0003
Chang Liu 0030
Qixiang Ye
Xiangyang Ji
Liang Lin

A codebook designed for learning discrete distributions in latent space has demonstrated state-of-the-art results on generation tasks. This inspires us to explore what distribution of codebook is better. Following the spirit of Kepler’s Conjecture, we cast the codebook training as solving the sphere packing problem and derive a Kepler codebook with a compact and structured distribution to obtain a codebook for image representations. Furthermore, we implement the Kepler codebook training by simply employing this derived distribution as regularization and using the codebook partition method. We conduct extensive experiments to evaluate our trained codebook for image reconstruction and generation on natural and human face datasets, respectively, achieving significant performance improvement. Besides, our Kepler codebook has demonstrated superior performance when evaluated across datasets and even for reconstructing images with different resolutions. Our trained models and source codes will be publicly released.

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AAAI Conference 2023 Conference Paper

Scene Graph to Image Synthesis via Knowledge Consensus

Yang Wu
Pengxu Wei
Liang Lin

In this paper, we study graph-to-image generation conditioned exclusively on scene graphs, in which we seek to disentangle the veiled semantics between knowledge graphs and images. While most existing research resorts to laborious auxiliary information such as object layouts or segmentation masks, it is also of interest to unveil the generality of the model with limited supervision, moreover, avoiding extra cross-modal alignments. To tackle this challenge, we delve into the causality of the adversarial generation process, and reason out a new principle to realize a simultaneous semantic disentanglement with an alignment on target and model distributions. This principle is named knowledge consensus, which explicitly describes a triangle causal dependency among observed images, graph semantics and hidden visual representations. The consensus also determines a new graph-to-image generation framework, carried on several adversarial optimization objectives. Extensive experimental results demonstrate that, even conditioned only on scene graphs, our model surprisingly achieves superior performance on semantics-aware image generation, without losing the competence on manipulating the generation through knowledge graphs.

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AAAI Conference 2021 Conference Paper

Deductive Learning for Weakly-Supervised 3D Human Pose Estimation via Uncalibrated Cameras

Xipeng Chen
Pengxu Wei
Liang Lin

Without prohibitive and laborious 3D annotations, weaklysupervised 3D human pose methods mainly employ the model regularization with geometric projection consistency or geometry estimation from multi-view images. Nevertheless, those approaches explicitly need known parameters of calibrated cameras, exhibiting a limited model generalization in various realistic scenarios. To mitigate this issue, in this paper, we propose a Deductive Weakly-Supervised Learning (DWSL) for 3D human pose machine. Our DWSL firstly learns latent representations on depth and camera pose for 3D pose reconstruction. Since weak supervision usually causes ill-conditioned learning or inferior estimation, our DWSL introduces deductive reasoning to make an inference for human pose from a view to another and develops a reconstruction loss to demonstrate what the model learns and infers is reliable. This learning by deduction strategy employs the view-transform demonstration and structural rules derived from depth, geometry and angle constraints, which improves the reliability of the model training with weak supervision. On three 3D human pose benchmarks, we conduct extensive experiments to evaluate our proposed method, which achieves superior performance in comparison with state-of-the-art weak-supervised methods. Particularly, our model shows an appealing potential for learning from 2D data captured in dynamic outdoor scenes, which demonstrates promising robustness and generalization in realistic scenarios. Our code is publicly available at https: //github. com/Xipeng- Chen/DWSL-3D-pose.

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