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Contrastive Learning for Robust Android Malware Familial Classification

IEEE Transactions on Dependable and Secure Computing 2022年 1-14页

作者： Wu, Yueming Dou, Shihan Zou, Deqing Yang, Wei Qiang, Weizhong Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China University of Texas at Dallas Dallas USA National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware family analysis can be achieved through contrastive learning. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and other types of malware. Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps can be obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining a 98.4% F1 on classifying 69,421 obfuscated malware samples. IEEE

关键词： Semantics

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Unified Lexical Representation for Interpretable Visual-Language Alignment 38

Unified Lexical Representation for Interpretable Visual-Lang...

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38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Li, Yifan Wang, Yikai Fu, Yanwei Ru, Dongyu Zhang, Zheng He, Tong Fudan University China Amazon Web Services United States School of Data Science in Fudan Fudan ISTBI-ZJNU Algorithm Center for Brain-inspired Intelligence Shanghai Key Lab of Intelligent Information Processing and Technology Innovation Center of Calligraphy and Painting Digital Generation Ministry of Culture and Tourism China

Visual-Language Alignment (VLA) has gained a lot of attention since CLIP's groundbreaking work. Although CLIP performs well, the typical direct latent feature alignment lacks clarity in its representation and similarity scores. On the other hand, lexical representation, a vector whose element represents the similarity between the sample and a word from the vocabulary, is a natural sparse representation and interpretable, providing exact matches for individual words. However, lexical representations are difficult to learn due to no ground-truth supervision and false-discovery issues, and thus requires complex design to train effectively. In this paper, we introduce LexVLA, a more interpretable VLA framework by learning a unified lexical representation for both modalities without complex design. We use DINOv2 as our visual model for its local-inclined features and Llama 2, a generative language model, to leverage its in-context lexical prediction ability. To avoid the false discovery, we propose an overuse penalty to refrain the lexical representation from falsely frequently activating meaningless words. We demonstrate that these two pre-trained uni-modal models can be well-aligned by fine-tuning on the modest multi-modal dataset and avoid intricate training configurations. On cross-modal retrieval benchmarks, LexVLA, trained on the CC-12M multi-modal dataset, outperforms baselines fine-tuned on larger datasets (e.g., YFCC15M) and those trained from scratch on even bigger datasets (e.g., 1.1B data, including CC-12M). We conduct extensive experiments to analyze LexVLA. Codes are available at https://***/Clementine24/LexVLA. © 2024 Neural information processing systems foundation. All rights reserved.

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DuMo: Dual Encoder Modulation Network for Precise Concept Erasure 39

DuMo: Dual Encoder Modulation Network for Precise Concept Er...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Han, Feng Chen, Kai Gong, Chao Wei, Zhipeng Chen, Jingjing Jiang, Yu-Gang Shanghai Key Lab of Intell. Info. Processing School of Computer Science Fudan University China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China

ISBN: (纸本)157735897X

The exceptional generative capability of text-to-image models has raised substantial safety concerns regarding the generation of Not-Safe-For-Work (NSFW) content and potential copyright infringement. To address these concerns, previous methods safeguard the models by eliminating inappropriate concepts. Nonetheless, these models alter the parameters of the backbone network and exert considerable influences on the structural (low-frequency) components of the image, which undermines the model’s ability to retain non-target concepts. In this work, we propose our Dual encoder Modulation network (DuMo), which achieves precise erasure of inappropriate target concepts with minimum impairment to non-target concepts. In contrast to previous methods, DuMo employs the Eraser with PRior Knowledge (EPR) module which modifies the skip connection features of the U-NET and primarily achieves concept erasure on details (high-frequency) components of the image. To minimize the damage to non-target concepts during erasure, the parameters of the backbone U-NET are frozen and the prior knowledge from the original skip connection features is introduced to the erasure process. Meanwhile, the phenomenon is observed that distinct erasing preferences for the image structure and details are demonstrated by the EPR at different timesteps and layers. Therefore, we adopt a novel Time-Layer MOdulation process (TLMO) that adjusts the erasure scale of EPR module’s outputs across different layers and timesteps, automatically balancing the erasure effects and model’s generative ability. Our method achieves state-of-the-art performance on Explicit Content Erasure (detecting only 34 nude parts), Cartoon Concept Removal (with an average LPIPS_da of 0.428, 0.113 higher than SOTA at 0.315), and Artistic Style Erasure (with an average LPIPS_da of 0.387, 0.088 higher than SOTA at 0.299), clearly outperforming alternative methods. © 2025, Association for the Advancement of Artificial Intel

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Learning Open-Vocabulary Semantic Segmentation Models From Natural Language Supervision

Learning Open-Vocabulary Semantic Segmentation Models From N...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Jilan Xu Junlin Hou Yuejie Zhang Rui Feng Yi Wang Yu Qiao Weidi Xie Shanghai Key Lab of Intelligent Information Processing School of Computer Science Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University Shanghai AI Laboratory CMIC Shanghai Jiao Tong University

This paper considers the problem of open-vocabulary semantic segmentation (OVS), that aims to segment objects of arbitrary classes beyond a pre-defined, closed-set categories. The main contributions are as follows: First, we propose a transformer-based model for OVS, termed as OVSegmentor, which only exploits web-crawled imagetext pairs for pre-training without using any mask annotations. OVSegmentor assembles the image pixels into a set of learnable group tokens via a slotattention based binding module, then aligns the group tokens to corresponding caption embeddings. Second, we propose two proxy tasks for training, namely masked entity completion and cross-image mask consistency. The former aims to infer all masked entities in the caption given group tokens, that enables the model to learn fine-grained alignment between visual groups and text entities. The latter enforces consistent mask predictions between images that contain shared entities, encouraging the model to learn visual invariance. Third, we construct CC4M dataset for pre-training by filtering CC12M with frequently appeared entities, which significantly improves training efficiency. Fourth, we perform zero-shot transfer on four benchmark datasets, PASCAL VOC, PASCAL Context, COCO Object, and ADE20K. OVSegmentor achieves superior results over state-of-the-art approaches on PASCAL VOC using only 3% data (4M vs 134M) for pre-training.

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Computing Resource Allocation for Blockchain-Based Mobile Edge Computing

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computer Modeling in Engineering & sciences 2024年第7期140卷 863-885页

作者： Wanbo Zhang Yuqi Fan Jun Zhang Xu Ding Jung Yoon Kim School of Computer Science and Information Engineering Intelligent Interconnected Systems Laboratory of Anhui ProvinceHefei University of TechnologyHefei230601China Anhui Province Key Lab of Aerospace Structural Parts Forming Technology and Equipment Hefei University of TechnologyHefei230009China College of Future Industry Gachon UniversitySeongnam13120South Korea

Users and edge servers are not fullymutually trusted inmobile edge computing(MEC),and hence blockchain can be introduced to provide *** blockchain-basedMEC,each edge server functions as a node in bothMEC and blockchain,processing users’tasks and then uploading the task related information to the *** is,each edge server runs both users’offloaded tasks and blockchain tasks *** that there is a trade-off between the resource allocation for MEC and blockchain ***,the allocation of the resources of edge servers to the blockchain and theMEC is crucial for the processing delay of blockchain-based *** of the existing research tackles the problem of resource allocation in either blockchain or MEC,which leads to unfavorable performance of the blockchain-based MEC *** this paper,we study how to allocate the computing resources of edge servers to the MEC and blockchain tasks with the aimtominimize the total systemprocessing *** the problem,we propose a computing resource Allocation algorithmfor Blockchain-based MEC(ABM)which utilizes the Slater’s condition,Karush-Kuhn-Tucker(KKT)conditions,partial derivatives of the Lagrangian function and subgradient projection method to obtain the *** results show that ABM converges and effectively reduces the processing delay of blockchain-based MEC.

关键词： Mobile edge computing blockchain resource allocation

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PLAYING TECHNIQUE DETECTION BY FUSING NOTE ONSET information IN GUZHENG PERFORMANCE 23

PLAYING TECHNIQUE DETECTION BY FUSING NOTE ONSET INFORMATION...

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23rd International Society for Music information Retrieval Conference, ISMIR 2022

作者： Li, Dichucheng Wu, Yulun Li, Qinyu Zhao, Jiahao Yu, Yi Xia, Fan Li, Wei School of Computer Science and Technology Fudan University Shanghai China College of Experimental Art Sichuan Conservatory of Music Sichuan China Tokyo Japan Shanghai Key Laboratory of Intelligent Information Processing Fudan University China

ISBN: (纸本)9781732729926

The Guzheng is a kind of traditional Chinese instruments with diverse playing techniques. Instrument playing techniques (IPT) play an important role in musical performance. However, most of the existing works for IPT detection show low efficiency for variable-length audio and provide no assurance in the generalization as they rely on a single sound bank for training and testing. In this study, we propose an end-to-end Guzheng playing technique detection system using Fully Convolutional Networks that can be applied to variable-length audio. Because each Guzheng playing technique is applied to a note, a dedicated onset detector is trained to divide an audio into several notes and its predictions are fused with frame-wise IPT predictions. During fusion, we add the IPT predictions frame by frame inside each note and get the IPT with the highest probability within each note as the final output of that note. We create a new dataset named GZ_IsoTech from multiple sound banks and real-world recordings for Guzheng performance analysis. Our approach achieves 87.97% in frame-level accuracy and 80.76% in note-level F1-score, outperforming existing works by a large margin, which indicates the effectiveness of our proposed method in IPT detection. © D. Li, Y. Wu, Q. Li, J. Zhao, Y. Yu, F. Xia, and W. Li.

关键词： Audio acoustics

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A Scalable Sparse Transformer Model for Singing Melody Extraction

A Scalable Sparse Transformer Model for Singing Melody Extra...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Shuai Yu Jun Liu Yi Yu Wei Li School of Computer Science and Technology Donghua University Shanghai China National Institute of Informatics (NII) Tokyo Japan School of Computer Science and Technology Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China

Extracting the melody of a singing voice is an essential task within the realm of music information retrieval (MIR). Recently, transformer based models have drawn great attention in the field of MIR. However, due to the expensive computation cost and extensive parameters, it is difficult to train and deploy a transformer-based model for practical singing melody extraction. In this paper, we propose a simple yet effective scalable sparse transformer for singing melody extraction. To be specific, we first propose to employ a sparse transformer to reduce computation cost and the amount of parameters. Then, we proposed to scale the self-attention region of the sparse transformer in the spectrogram to obtain more accurate performance. Moreover, we propose to combine a scalable sparse transformer (S 2 Former) with CNN-based model to extract global and local features in the spectrogram. The proposed scalable transformer model can achieve a better balance between a standard transformer and a sparse transformer. To better fuse the features from transformer and CNN, we further propose a transformer-CNN fusion (TCF) module to combine significant features from transformer and CNN. The proposed model obtains state-of-the-art results on several public datasets. The conducted experiments confirm the effectiveness of the model we proposed.

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ReToMe-VA: Recursive Token Merging for Video Diffusion-based Unrestricted Adversarial Attack

arXiv

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arXiv 2024年

作者： Gao, Ziyi Chen, Kai Wei, Zhipeng Mou, Tingshu Chen, Jingjing Tan, Zhiyu Li, Hao Jiang, Yu-Gang Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University China Artificial Intelligence Innovation and Incubation Institute Fudan University China

Recent diffusion-based unrestricted attacks generate imperceptible adversarial examples with high transferability compared to previous unrestricted attacks and restricted attacks. However, existing works on diffusion-based unrestricted attacks are mostly focused on images yet are seldom explored in videos. In this paper, we propose the Recursive Token Merging for Video Diffusion-based Unrestricted Adversarial Attack (ReToMe-VA), which is the first framework to generate imperceptible adversarial video clips with higher transferability. Specifically, to achieve spatial imperceptibility, ReToMe-VA adopts a Timestep-wise Adversarial Latent Optimization (TALO) strategy that optimizes perturbations in diffusion models’ latent space at each denoising step. TALO offers iterative and accurate updates to generate more powerful adversarial frames. TALO can further reduce memory consumption in gradient computation. Moreover, to achieve temporal imperceptibility, ReToMe-VA introduces a Recursive Token Merging (ReToMe) mechanism by matching and merging tokens across video frames in the self-attention module, resulting in temporally consistent adversarial videos. ReToMe concurrently facilitates inter-frame interactions into the attack process, inducing more diverse and robust gradients, thus leading to better adversarial transferability. Extensive experiments demonstrate the efficacy of ReToMe-VA, particularly in surpassing state-of-the-art attacks in adversarial transferability by more than 14.16% on average. Copyright © 2024, The Authors. All rights reserved.

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Investigation of Singing Voice Separation for Singing Voice Detection in Polyphonic Music 9th

Investigation of Singing Voice Separation for Singing Voic...

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9th Conference on Sound and Music Technology, CSMT 2021

作者： Sun, Yifu Zhang, Xulong Chen, Xi Yu, Yi Li, Wei School of Computer Science and Technology Fudan University Shanghai200438 China Co. Ltd. Shenzhen518000 China Digital Content and Media Sciences Research Division National Institute of Informatics Tokyo Chiyoda163-8001 Japan Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai200433 China

ISBN: (纸本)9789811947025

Singing voice detection (SVD), to recognize vocal parts in the song, is an essential task in music information retrieval (MIR). The task remains challenging since singing voice varies and intertwines with the accompaniment music, especially for some complicated polyphonic music such as choral music recordings. To address this problem, we investigate singing voice detection while discarding the interference from the accompaniment. The proposed SVD has two steps: i. The singing voice separation (SVS) technique is first utilized to filter out the singing voice’s potential part coarsely. ii. Upon the continuity of vocal in the time domain, Long-term Recurrent Convolutional Networks (LRCN) is used to learn compositional features. Moreover, to eliminate the outliers, we choose to use a median filter for time-domain smoothing. Experimental results show that the proposed method outperforms the existing state-of-the-art works on two public datasets, the Jamendo Corpus and the RWC pop dataset. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Music

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Global Convergence Guarantees of (A)GIST for a Family of Nonconvex Sparse Learning Problems

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IEEE Transactions on Cybernetics 2022年第5期52卷 3276-3288页

作者： Zhang, Hengmin Qian, Feng Shang, Fanhua Du, Wenli Qian, Jianjun Yang, Jian East China University of Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education School of Information Science and Engineering Shanghai200237 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xi'an710071 China Peng Cheng Laboratory Shenzhen518066 China Nanjing University of Science and Technology Pca Laboratory Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Nanjing210094 China Nanjing University of Science and Technology Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing210094 China

In recent years, most of the studies have shown that the generalized iterated shrinkage thresholdings (GISTs) have become the commonly used first-order optimization algorithms in sparse learning problems. The nonconvex relaxations of the \ell _{0} -norm usually achieve better performance than the convex case (e.g., \ell _{1} -norm) since the former can achieve a nearly unbiased solver. To increase the calculation efficiency, this work further provides an accelerated GIST version, that is, AGIST, through the extrapolation-based acceleration technique, which can contribute to reduce the number of iterations when solving a family of nonconvex sparse learning problems. Besides, we present the algorithmic analysis, including both local and global convergence guarantees, as well as other intermediate results for the GIST and AGIST, denoted as (A)GIST, by virtue of the Kurdyka-Lojasiewica (KL) property and some milder assumptions. Numerical experiments on both synthetic data and real-world databases can demonstrate that the convergence results of objective function accord to the theoretical properties and nonconvex sparse learning methods can achieve superior performance over some convex ones. © 2013 IEEE.

关键词： Optimization

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