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Shikui Wei

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8 papers

1 author row

AAAI Conference 2024 Conference Paper

Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Domain Learning

Chuangchuang Tan
Yao Zhao
Shikui Wei
Guanghua Gu
Ping Liu
Yunchao Wei

This research addresses the challenge of developing a universal deepfake detector that can effectively identify unseen deepfake images despite limited training data. Existing frequency-based paradigms have relied on frequency-level artifacts introduced during the up-sampling in GAN pipelines to detect forgeries. However, the rapid advancements in synthesis technology have led to specific artifacts for each generation model. Consequently, these detectors have exhibited a lack of proficiency in learning the frequency domain and tend to overfit to the artifacts present in the training data, leading to suboptimal performance on unseen sources. To address this issue, we introduce a novel frequency-aware approach called FreqNet, centered around frequency domain learning, specifically designed to enhance the generalizability of deepfake detectors. Our method forces the detector to continuously focus on high-frequency information, exploiting high-frequency representation of features across spatial and channel dimensions. Additionally, we incorporate a straightforward frequency domain learning module to learn source-agnostic features. It involves convolutional layers applied to both the phase spectrum and amplitude spectrum between the Fast Fourier Transform (FFT) and Inverse Fast Fourier Transform (iFFT). Extensive experimentation involving 17 GANs demonstrates the effectiveness of our proposed method, showcasing state-of-the-art performance (+9.8\%) while requiring fewer parameters. The code is available at https://github.com/chuangchuangtan/FreqNet-DeepfakeDetection.

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AAAI Conference 2024 Conference Paper

Learning Invariant Inter-pixel Correlations for Superpixel Generation

Sen Xu
Shikui Wei
Tao Ruan
Lixin Liao

Deep superpixel algorithms have made remarkable strides by substituting hand-crafted features with learnable ones. Nevertheless, we observe that existing deep superpixel methods, serving as mid-level representation operations, remain sensitive to the statistical properties (e.g., color distribution, high-level semantics) embedded within the training dataset. Consequently, learnable features exhibit constrained discriminative capability, resulting in unsatisfactory pixel grouping performance, particularly in untrainable application scenarios. To address this issue, we propose the Content Disentangle Superpixel (CDS) algorithm to selectively separate the invariant inter-pixel correlations and statistical properties, i.e., style noise. Specifically, We first construct auxiliary modalities that are homologous to the original RGB image but have substantial stylistic variations. Then, driven by mutual information, we propose the local-grid correlation alignment across modalities to reduce the distribution discrepancy of adaptively selected features and learn invariant inter-pixel correlations. Afterwards, we perform global-style mutual information minimization to enforce the separation of invariant content and train data styles. The experimental results on four benchmark datasets demonstrate the superiority of our approach to existing state-of-the-art methods, regarding boundary adherence, generalization, and efficiency. Code and pre-trained model are available at https://github.com/rookiie/CDSpixel.

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AAAI Conference 2024 Conference Paper

Lyapunov-Stable Deep Equilibrium Models

Haoyu Chu
Shikui Wei
Ting Liu
Yao Zhao
Yuto Miyatake

Deep equilibrium (DEQ) models have emerged as a promising class of implicit layer models, which abandon traditional depth by solving for the fixed points of a single nonlinear layer. Despite their success, the stability of the fixed points for these models remains poorly understood. By considering DEQ models as nonlinear dynamic systems, we propose a robust DEQ model named LyaDEQ with guaranteed provable stability via Lyapunov theory. The crux of our method is ensuring the Lyapunov stability of the DEQ model's fixed points, which enables the proposed model to resist minor initial perturbations. To avoid poor adversarial defense due to Lyapunov-stable fixed points being located near each other, we orthogonalize the layers after the Lyapunov stability module to separate different fixed points. We evaluate LyaDEQ models under well-known adversarial attacks, and experimental results demonstrate significant improvement in robustness. Furthermore, we show that the LyaDEQ model can be combined with other defense methods, such as adversarial training, to achieve even better adversarial robustness.

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IJCAI Conference 2024 Conference Paper

Structure-Preserving Physics-Informed Neural Networks with Energy or Lyapunov Structure

Haoyu Chu
Yuto Miyatake
Wenjun Cui
Shikui Wei
Daisuke Furihata

Recently, there has been growing interest in using physics-informed neural networks (PINNs) to solve differential equations. However, the preservation of structure, such as energy and stability, in a suitable manner has yet to be established. This limitation could be a potential reason why the learning process for PINNs is not always efficient and the numerical results may suggest nonphysical behavior. Besides, there is little research on their applications on downstream tasks. To address these issues, we propose structure-preserving PINNs to improve their performance and broaden their applications for downstream tasks. Firstly, by leveraging prior knowledge about the physical system, a structure‐preserving loss function is designed to assist the PINN in learning the underlying structure. Secondly, a framework that utilizes structure-preserving PINN for robust image recognition is proposed. Here, preserving the Lyapunov structure of the underlying system ensures the stability of the system. Experimental results demonstrate that the proposed method improves the numerical accuracy of PINNs for partial differential equations (PDEs). Furthermore, the robustness of the model against adversarial perturbations in image data is enhanced.

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EAAI Journal 2024 Journal Article

Unsupervised node representation learning of pure graph via symmetric cumulative sampling strategy

Huaxin Pang
Shikui Wei
Tianzhi Jia
Yufeng Zhao
Yao Zhao

It is very significant to learn graph representation by using only the network topology information in many application scenarios, where attributes of nodes are not available or not easy to obtain. Although existing approaches have achieved impressive performance, they cannot deterministically involve important nodes related to the current node in the representation learning process, leading to a lack of robustness. To address this problem, we propose a novel symmetric cumulative sampling strategy for unsupervised graph representation learning via multi-step propagation and aggregation sampling, named MPMA. According to generated hierarchical transition probabilities, MPMA is able to select crucial nodes and construct pyramidal node subsets from both propagation and aggregation perspectives. Based on the constructed node subsets, an efficient algorithm is employed to search the shortest paths between the current node and each important node to preserve sufficient interaction of nodes. Extensive experiments on diverse application tasks verify the effectiveness and robustness of the proposed method.

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AAAI Conference 2021 Conference Paper

GradingNet: Towards Providing Reliable Supervisions for Weakly Supervised Object Detection by Grading the Box Candidates

Qifei Jia
Shikui Wei
Tao Ruan
Yufeng Zhao
Yao Zhao

Weakly-Supervised Object Detection (WSOD) aims at training a model with limited and coarse annotations for precisely locating the regions of objects. Existing works solve the W- SOD problem by using a two-stage framework, i. e. , generating candidate bounding boxes with weak supervision information and then refining them by directly employing supervised object detection models. However, most of such works focus mainly on the performance-boosting of the first stage, while ignoring the better usage of generated candidate bounding boxes. To address this issue, we propose a new two-stage framework for WSOD, named GradingNet, which can make good use of the generated candidate bounding boxes. Specifically, the proposed GradingNet consists of two modules: Boxes Grading Module (BGM) and Informative Boosting Module (IBM). BGM generates proposals of the bounding boxes by using standard one-stage weakly-supervised methods, then utilizes the Inclusion Principle to pick out highlyreliable boxes and evaluate the grade of each box. With the above boxes and their grade information, an effective anchor generator and a grade-aware loss are carefully designed to train the IBM. Taking the advantages of the grade information, our GradingNet achieves state-of-the-art performance on COCO, VOC 2007, and VOC 2012 benchmarks.

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AAAI Conference 2019 Conference Paper

Devil in the Details: Towards Accurate Single and Multiple Human Parsing

Tao Ruan
Ting Liu
Zilong Huang
Yunchao Wei
Shikui Wei
Yao Zhao

Human parsing has received considerable interest due to its wide application potentials. Nevertheless, it is still unclear how to develop an accurate human parsing system in an efficient and elegant way. In this paper, we identify several useful properties, including feature resolution, global context information and edge details, and perform rigorous analyses to reveal how to leverage them to benefit the human parsing task. The advantages of these useful properties finally result in a simple yet effective Context Embedding with Edge Perceiving (CE2P) framework for single human parsing. Our CE2P is end-to-end trainable and can be easily adopted for conducting multiple human parsing. Benefiting the superiority of CE2P, we won the 1st places on all three human parsing tracks in the 2nd Look into Person (LIP) Challenge. Without any bells and whistles, we achieved 56. 50% (mIoU), 45. 31% (mean APr ) and 33. 34% (APp 0. 5) in Track 1, Track 2 and Track 5, which outperform the state-of-the-arts more than 2. 06%, 3. 81% and 1. 87%, respectively. We hope our CE2P will serve as a solid baseline and help ease future research in single/multiple human parsing. Code has been made available at https: //github. com/liutinglt/CE2P.

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TIST Journal 2016 Journal Article

Modality-Dependent Cross-Media Retrieval

Yunchao Wei
Yao Zhao
Zhenfeng Zhu
Shikui Wei
Yanhui Xiao
Jiashi Feng
Shuicheng Yan

In this article, we investigate the cross-media retrieval between images and text, that is, using image to search text (I2T) and using text to search images (T2I). Existing cross-media retrieval methods usually learn one couple of projections, by which the original features of images and text can be projected into a common latent space to measure the content similarity. However, using the same projections for the two different retrieval tasks (I2T and T2I) may lead to a tradeoff between their respective performances, rather than their best performances. Different from previous works, we propose a modality-dependent cross-media retrieval (MDCR) model, where two couples of projections are learned for different cross-media retrieval tasks instead of one couple of projections. Specifically, by jointly optimizing the correlation between images and text and the linear regression from one modal space (image or text) to the semantic space, two couples of mappings are learned to project images and text from their original feature spaces into two common latent subspaces (one for I2T and the other for T2I). Extensive experiments show the superiority of the proposed MDCR compared with other methods. In particular, based on the 4,096-dimensional convolutional neural network (CNN) visual feature and 100-dimensional Latent Dirichlet Allocation (LDA) textual feature, the mAP of the proposed method achieves the mAP score of 41.5%, which is a new state-of-the-art performance on the Wikipedia dataset.

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