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Mind Your Weight(s): A Large-scale Study on Insufficient Machine
  Learning Model Protection in Mobile Apps

Mind Your Weight(s): A Large-scale Study on Insufficient Machine Learning Model Protection in Mobile Apps

18 February 2020
Zhichuang Sun
Ruimin Sun
Long Lu
Alan Mislove
ArXivPDFHTML

Papers citing "Mind Your Weight(s): A Large-scale Study on Insufficient Machine Learning Model Protection in Mobile Apps"

38 / 38 papers shown
Title
FCGHunter: Towards Evaluating Robustness of Graph-Based Android Malware Detection
FCGHunter: Towards Evaluating Robustness of Graph-Based Android Malware Detection
Shiwen Song
Xiaofei Xie
Ruitao Feng
Qi Guo
Sen Chen
AAML
36
0
0
28 Apr 2025
THEMIS: Towards Practical Intellectual Property Protection for Post-Deployment On-Device Deep Learning Models
THEMIS: Towards Practical Intellectual Property Protection for Post-Deployment On-Device Deep Learning Models
Yujin Huang
Zhi Zhang
Qingchuan Zhao
Xingliang Yuan
Chunyang Chen
37
0
0
31 Mar 2025
ProDiF: Protecting Domain-Invariant Features to Secure Pre-Trained Models Against Extraction
ProDiF: Protecting Domain-Invariant Features to Secure Pre-Trained Models Against Extraction
Tong Zhou
Shijin Duan
Gaowen Liu
Charles Fleming
Ramana Rao Kompella
Shaolei Ren
Xiaolin Xu
AAML
60
0
0
17 Mar 2025
"Impressively Scary:" Exploring User Perceptions and Reactions to Unraveling Machine Learning Models in Social Media Applications
Jack West
Bengisu Cagiltay
Shirley Zhang
Jingjie Li
Kassem Fawaz
Suman Banerjee
65
0
0
05 Mar 2025
Stealthy Backdoor Attack to Real-world Models in Android Apps
Jiali Wei
Ming Fan
Xicheng Zhang
Wenjing Jiao
H. Wang
Ting Liu
AAML
26
0
0
03 Jan 2025
Towards Data Governance of Frontier AI Models
Towards Data Governance of Frontier AI Models
Jason Hausenloy
Duncan McClements
Madhavendra Thakur
72
1
0
05 Dec 2024
TEESlice: Protecting Sensitive Neural Network Models in Trusted
  Execution Environments When Attackers have Pre-Trained Models
TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models
Ding Li
Ziqi Zhang
Mengyu Yao
Y. Cai
Yao Guo
Xiangqun Chen
FedML
37
2
0
15 Nov 2024
A Novel Access Control and Privacy-Enhancing Approach for Models in Edge
  Computing
A Novel Access Control and Privacy-Enhancing Approach for Models in Edge Computing
Peihao Li
25
0
0
06 Nov 2024
SLIP: Securing LLMs IP Using Weights Decomposition
SLIP: Securing LLMs IP Using Weights Decomposition
Yehonathan Refael
Adam Hakim
Lev Greenberg
T. Aviv
S. Lokam
Ben Fishman
Shachar Seidman
40
3
0
15 Jul 2024
AuthNet: Neural Network with Integrated Authentication Logic
AuthNet: Neural Network with Integrated Authentication Logic
Yuling Cai
Fan Xiang
Guozhu Meng
Yinzhi Cao
Kai Chen
AAML
53
0
0
24 May 2024
TBNet: A Neural Architectural Defense Framework Facilitating DNN Model
  Protection in Trusted Execution Environments
TBNet: A Neural Architectural Defense Framework Facilitating DNN Model Protection in Trusted Execution Environments
Ziyu Liu
Tong Zhou
Yukui Luo
Xiaolin Xu
23
2
0
07 May 2024
Octopus v4: Graph of language models
Octopus v4: Graph of language models
Wei Chen
Zhiyuan Li
30
5
0
30 Apr 2024
GuaranTEE: Towards Attestable and Private ML with CCA
GuaranTEE: Towards Attestable and Private ML with CCA
S. Siby
Sina Abdollahi
Mohammad Maheri
Marios Kogias
Hamed Haddadi
35
7
0
29 Mar 2024
A Picture is Worth 500 Labels: A Case Study of Demographic Disparities
  in Local Machine Learning Models for Instagram and TikTok
A Picture is Worth 500 Labels: A Case Study of Demographic Disparities in Local Machine Learning Models for Instagram and TikTok
Jack West
Lea Thiemt
Shimaa Ahmed
Maggie Bartig
Kassem Fawaz
Suman Banerjee
29
4
0
27 Mar 2024
MirrorNet: A TEE-Friendly Framework for Secure On-device DNN Inference
MirrorNet: A TEE-Friendly Framework for Secure On-device DNN Inference
Ziyu Liu
Yukui Luo
Shijin Duan
Tong Zhou
Xiaolin Xu
FedML
17
10
0
16 Nov 2023
No Privacy Left Outside: On the (In-)Security of TEE-Shielded DNN
  Partition for On-Device ML
No Privacy Left Outside: On the (In-)Security of TEE-Shielded DNN Partition for On-Device ML
Ziqi Zhang
Chen Gong
Yifeng Cai
Yuanyuan Yuan
Bingyan Liu
Ding Li
Yao Guo
Xiangqun Chen
FedML
37
16
0
11 Oct 2023
Efficient Query-Based Attack against ML-Based Android Malware Detection
  under Zero Knowledge Setting
Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge Setting
Ping He
Yifan Xia
Xuhong Zhang
Shouling Ji
AAML
18
11
0
05 Sep 2023
Towards Real Smart Apps: Investigating Human-AI Interactions in
  Smartphone On-Device AI Apps
Towards Real Smart Apps: Investigating Human-AI Interactions in Smartphone On-Device AI Apps
Jason Ching Yuen Siu
Jieshan Chen
Yujin Huang
Zhenchang Xing
Chunyang Chen
11
0
0
03 Jul 2023
ModelObfuscator: Obfuscating Model Information to Protect Deployed
  ML-based Systems
ModelObfuscator: Obfuscating Model Information to Protect Deployed ML-based Systems
Mingyi Zhou
Xiang Gao
Jing Wu
John C. Grundy
Xiao Chen
Chunyang Chen
Li Li
AAML
31
12
0
01 Jun 2023
Beyond the Model: Data Pre-processing Attack to Deep Learning Models in
  Android Apps
Beyond the Model: Data Pre-processing Attack to Deep Learning Models in Android Apps
Ye Sang
Yujin Huang
Shuo Huang
Helei Cui
AAML
SILM
23
5
0
06 May 2023
NNSplitter: An Active Defense Solution for DNN Model via Automated
  Weight Obfuscation
NNSplitter: An Active Defense Solution for DNN Model via Automated Weight Obfuscation
Tong Zhou
Yukui Luo
Shaolei Ren
Xiaolin Xu
AAML
49
15
0
28 Apr 2023
Evil from Within: Machine Learning Backdoors through Hardware Trojans
Evil from Within: Machine Learning Backdoors through Hardware Trojans
Alexander Warnecke
Julian Speith
Janka Möller
Konrad Rieck
C. Paar
AAML
11
3
0
17 Apr 2023
A Light-weight Deep Learning Model for Remote Sensing Image
  Classification
A Light-weight Deep Learning Model for Remote Sensing Image Classification
L. D. Pham
Cam Le
Dat Ngo
A. Nguyen
Jasmin Lampert
Alexander Schindler
Ian Mcloughlin
31
2
0
25 Feb 2023
"Real Attackers Don't Compute Gradients": Bridging the Gap Between
  Adversarial ML Research and Practice
"Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practice
Giovanni Apruzzese
Hyrum S. Anderson
Savino Dambra
D. Freeman
Fabio Pierazzi
Kevin A. Roundy
AAML
31
75
0
29 Dec 2022
DeepTaster: Adversarial Perturbation-Based Fingerprinting to Identify
  Proprietary Dataset Use in Deep Neural Networks
DeepTaster: Adversarial Perturbation-Based Fingerprinting to Identify Proprietary Dataset Use in Deep Neural Networks
Seonhye Park
A. Abuadbba
Shuo Wang
Kristen Moore
Yansong Gao
Hyoungshick Kim
Surya Nepal
AAML
17
2
0
24 Nov 2022
A Robust and Low Complexity Deep Learning Model for Remote Sensing Image
  Classification
A Robust and Low Complexity Deep Learning Model for Remote Sensing Image Classification
Cam Le
L. D. Pham
Nghia Nvn
Truong Thao Nguyen
L. Trang
18
2
0
05 Nov 2022
Robust, General, and Low Complexity Acoustic Scene Classification
  Systems and An Effective Visualization for Presenting a Sound Scene Context
Robust, General, and Low Complexity Acoustic Scene Classification Systems and An Effective Visualization for Presenting a Sound Scene Context
L. D. Pham
Dusan Salovic
Anahid N. Jalali
Alexander Schindler
Khoa Tran
H. Vu
Phu X. Nguyen
16
5
0
16 Oct 2022
Understanding Real-world Threats to Deep Learning Models in Android Apps
Understanding Real-world Threats to Deep Learning Models in Android Apps
Zizhuang Deng
Kai Chen
Guozhu Meng
Xiaodong Zhang
Ke Xu
Yao Cheng
AAML
18
26
0
20 Sep 2022
Edge Security: Challenges and Issues
Edge Security: Challenges and Issues
Xin Jin
Charalampos Katsis
Fan Sang
Jiahao Sun
A. Kundu
Ramana Rao Kompella
39
8
0
14 Jun 2022
Automation Slicing and Testing for in-App Deep Learning Models
Automation Slicing and Testing for in-App Deep Learning Models
Hao Wu
Yuhang Gong
Xiaopeng Ke
Hanzhong Liang
Minghao Li
Fengyuan Xu
Yunxin Liu
Sheng Zhong
41
1
0
15 May 2022
Smart App Attack: Hacking Deep Learning Models in Android Apps
Smart App Attack: Hacking Deep Learning Models in Android Apps
Yujin Huang
Chunyang Chen
FedML
AAML
15
21
0
23 Apr 2022
Confidential Machine Learning Computation in Untrusted Environments: A
  Systems Security Perspective
Confidential Machine Learning Computation in Untrusted Environments: A Systems Security Perspective
Kha Dinh Duy
Taehyun Noh
Siwon Huh
Hojoon Lee
56
9
0
05 Nov 2021
Smart at what cost? Characterising Mobile Deep Neural Networks in the
  wild
Smart at what cost? Characterising Mobile Deep Neural Networks in the wild
Mario Almeida
Stefanos Laskaridis
Abhinav Mehrotra
L. Dudziak
Ilias Leontiadis
Nicholas D. Lane
HAI
109
44
0
28 Sep 2021
LEAP: TrustZone Based Developer-Friendly TEE for Intelligent Mobile Apps
LEAP: TrustZone Based Developer-Friendly TEE for Intelligent Mobile Apps
Lizhi Sun
Shuocheng Wang
Hao Wu
Yuhang Gong
Fengyuan Xu
Yunxin Liu
Hao Han
Sheng Zhong
20
9
0
04 Feb 2021
Robustness of on-device Models: Adversarial Attack to Deep Learning
  Models on Android Apps
Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android Apps
Yujin Huang
Han Hu
Chunyang Chen
AAML
FedML
72
33
0
12 Jan 2021
ShadowNet: A Secure and Efficient On-device Model Inference System for
  Convolutional Neural Networks
ShadowNet: A Secure and Efficient On-device Model Inference System for Convolutional Neural Networks
Zhichuang Sun
Ruimin Sun
Changming Liu
A. Chowdhury
Long Lu
S. Jha
FedML
29
18
0
11 Nov 2020
Slalom: Fast, Verifiable and Private Execution of Neural Networks in
  Trusted Hardware
Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware
Florian Tramèr
Dan Boneh
FedML
114
395
0
08 Jun 2018
Adversarial Machine Learning at Scale
Adversarial Machine Learning at Scale
Alexey Kurakin
Ian Goodfellow
Samy Bengio
AAML
261
3,109
0
04 Nov 2016
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