robustness and transferability of universal attacks on compressed models

Robustness of ML Models Optical illusions trick human brains Can ML models be tricked? Fredrikson, M., Jha, S. & Ristenpart, T. Model inversion attacks that exploit confidence information and basic countermeasures. ArXiv abs/2012.06024(2020). I obtained my PhD from the signal processing laboratory in EPFL in 2016, and my M.Sc. both targeted [6] and also universal approaches [7]. 2017. • 3D Graph-S2Net: Shape-Aware Self-Ensembling Network for Semi-Supervised Segmentation with Bilateral Graph Convolution. However, in most cases, the attack is of laundering-type, consisting in the application of a post-processing operation, e.g., a geometric transformation, filtering or compression. Compressed Indexes for Fast Search of Semantic Data. 2. Motivated by this observation, we propose a regularization technique which enforces the attentions to be well aligned via the knowledge transfer mechanism, thereby encouraging the robustness. (2020) considered random feature selection strategy that can improve the security and robustness of forensic detectors for the general model and standard ML-based to mitigate the adversarial dangerousness attacks. Adversarial Attacks Against Multi-Agent Communication (Best Paper Award Nomination, 11 out of 271 papers) Clare Lyle, Mark Rowland, Georg Ostrovski, Will Dabney. "Robustness and Transferability of Universal Attacks on Compressed Models" (AAAI'21 Workshop) We encourage you to explore these Python notebooks to generate and evaluate your own UAPs. Universal ATM Card System Harshal M. Bajad1Sandeep E. Deshmukh2Pradnya R. Chaugule3Mayur S. Tambade4 1,2,3,4Students, Department of Information Technology, Genba Sopanrao Moze College of Engineering, Pune, India. Research interests: I am broadly interested in challenging problems related to computer vision and machine learning. Matachana A, Co KT, Munoz Gonzalez L, Martinez D, Lupu E close, Robustness and transferability of universal attacks on compressed models, AAAI 2021 Workshop, AAAI. Qi Liu, Tao Liu, Zihao Liu, Yanzhi Wang, Yier Jin, and Wujie Wen, “Security analysis and enhancement of model compressed deep learning systems under adversarial attacks,” to appear in Proc. High-robustness, low-transferability fingerprinting of neural networks. Barni et al. Create an MIFace attack instance. Please note that all publication formats (PDF, ePub, and Zip) are posted as they become available from our vendor. Robustness and Transferability of Universal Attacks on Compressed Models. There exists a correlation between clean model accuracy and UER of untargeted white-box attacks 2. 2!! Daz Studio allows you to easily create custom scenes and characters in seconds. Qualcomm’s aptX Lossless is the first codec to claim fully lossless Bluetooth audio, bit-exact playback of CD-quality files, and also appears to … In this tutorial, you will discover how to add noise to deep … Analyzing machine-learned representations: A natural language case study. … You're an original, and your art is too with Daz Studio. Seunghyun!Lee,!Byung!Cheol!Song! Else, permanently replace w i with the c k that results in the minimal correct classification probability, and repeat the prior two steps with w i + 1. ACM/IEEE 23rd Asia and South Pacific Design Automation Conference (ASP-DAC), Jan. 2018, pp. Universal adversarial perturbations. Google Scholar Cross Ref; Yinpeng Dong, Hang Su, Baoyuan Wu, Zhifeng Li, Wei Liu, Tong Zhang, and Jun Zhu. examples to evaluate the robustness of defenses. High Quality Test Generation at the Register Transfer Level. Resource-limited Wireless Sensor Networks (WSNs) as a … It suggests that adversarial examples are universal and not the results of over tting or speci c to training set. I was awarded the IBM PhD Fellowship awards for the academic years 2013-2014 and 2015-2016. In a backdoor attack on a machine learning model, an adversary produces a model that performs well on normal inputs but outputs targeted misclassifications on inputs containing a small trigger pattern. Shenzhen, China. "Robustness and Transferability of Universal Attacks on Compressed Models" (AAAI'21 Workshop) We encourage you to explore these Python notebooks to generate and evaluate your own UAPs. • 2D Histology Meets 3D Topology: Cytoarchitectonic Brain Mapping with Graph Neural Networks. Robustness and Transferability of Universal Attacks on Compressed Models kenny-co/sgd-uap-torch • • 10 Dec 2020 In this work, we analyze the effect of various compression techniques to UAP attacks, including different forms of pruning and quantization. UAPs for Texture vs. Rahman, S. Hou, Dongbin classifier – Target classifier. Boosting adversarial attacks with momentum. (acceptance rate 1 3.9 %). Transfer-ability [46], [11] is the well-known property that adversarial examples on one model are often also adversarial on another model. causes the target model to misclassify, the attack is successful (done). By converting dense models into sparse ones, pruning appears to be a promising solution to reducing the computation/memory cost. Index Terms adversarial attacks, deep learning, robust clas-siers, sound event classication. Adversarial examples are regarded as a security threat to deep learning models, and there are many ways to generate them. If you are new to this topic, we suggest running the notebooks for the CIFAR-10 UAPs first. What about surfaces and point clouds? Detection and recovery against deep neural network fault injection attacks based on contrastive learning. [5:15] Benefit of deep learning with non-convex noisy gradient descent: Provable excess risk bound and superiority to kernel methods. We study the extent to which adversarial perturbations transfer across different models and propose techniques to improve the transferability of adversarial examples. H Phan, Y Xie, S Liao, J Chen, B Yuan. Mechanized Proofs of Adversarial Complexity and Application to Universal Composability. 2021 IEEE International Conference on Multimedia and Expo (ICME) July 5 2021 to July 9 2021. Yucheng Shi, Yahong Han, Quanxin Zhang, Xiaohui Kuang Pattern Recognition, Current iterative attacks use a fixed step size for each noise-adding step, making further investigation into the effect of variable step size on model robustness ripe for exploration. Use a powerful attack (such as the ones proposed in this paper) to evaluate the robustness of the secured model directly. Since a defense that prevents our L 2attack will prevent our other attacks, defenders should make sure to establish robustness against the L Data Mining can be the most efficient technique to … 6. To cope with such attacks, countermeasures were developed in turn, based on a second-order analysis [8], [9]. A new property of cryptosystems is proposed, Indistinguishability under Partially Chosen Plaintext Attack (IND-PCPA), along with an attack model that utilizes it. Internet of Things (IoTs) are increasingly widespread in the field of health care, smart city and smart home application, industrial and agricultural monitoring, automation, etc. Volume , Issue 01. Many deep learning applications, e.g. Adversarial examples and poisoning attacks have become indisputable threats to the security of modern AI systems based on deep neural networks (DNNs). 1994. UAPs for Texture vs. The workshop serves as a forum for researchers from a variety of fields working on machine learning to share and discuss their latest findings. Emil Lupu is a Professor of Computer Systems in the Department of Computing at Imperial College London. Detection and recovery against deep neural network fault injection attacks based on contrastive learning. Model compression is a widely-used approach for reducing the size of deep learning models without much accuracy loss, enabling resource-hungry models … Optical Illusions. In this work, we perform the first systematic study on local model poisoning attacks to federated learning. Related Work 2.1. about a model. 60. the automated vehicles and face Federated learning (FL) [] emerges recently along with the rising privacy concerns in the use of large-scale dataset and cloud-based deep learning [].The basic components in a federated learning process are a central node and several client nodes.The central node holds the global model and receives the trained parameters from … Gent, Kelson. Intermittent Visual Servoing: Effciently Learning Policies Robust to Instrument Changes for High-precision Surgical Manipulation (EECS-2021-104) Samuel Paradis, Ken Goldberg and Joseph Gonzalez D. Transferability Recent work has shown that an adversarial example for one model will often transfer to be an adversarial on a different model, even if they are trained on different sets of training data [46], [11], and even if they use entirely different algorithms (i.e., adversarial examples on neural networks transfer to random forests [37]). Can we even define a single spatial perturbation for an entire collection of shapes? 721-726. [IJCAI] Z. Xiao, Y Xie, J. Chen and B. Yuan, "Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models," accepted in International Joint Conferences on Artificial Intelligence (IJCAI), Aug. 2021. [5:25] Tent: Fully Test-Time Adaptation by Entropy Minimization. Analysis!and!Graph!Neural!Network! It does this by modeling an universal first-order attack through the inner maximization 2486. Isospectralization: Applications * Moosavi-Dezfooli et al. We will comply with all removal requests . On the Robustness of Deep Learning Models to Universal Adversarial Attack. Robustness and Transferability of Universal Attacks on Compressed Models Arnaud Van Looveren and Giovanni Vacanti. IEEE Transactions on Dependable and Secure Computing - Table of Contents. Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5412-5419. Robustness and Transferability of Universal Attacks on Compressed Models AG Matachana, KT Co, L Muñoz-González, D Martinez, EC Lupu AAAI 2021: Towards Robust, Secure, and Efficient Machine Learning , 2020 Efficient decision-based black-box adversarial attacks on face recognition. ! 2020. THIS IS NOT A GET RICH SCHEME.Why work for somebody else when you can become rich inside … Regularization Can Help Mitigate Poisoning Attacks... with the Right Hyperparameters May 23, 2021 Javier Carnerero-Cano, Luis Muñoz-González, Phillippa Spencer, Emil C. Lupu Model/Code API Access Call/Text an Expert Dear Friend , Especially for you - this red-hot intelligence. We test the robustness of compressed models to white-box and transfer attacks, comparing them with their uncompressed counterparts on CIFAR-10 and SVHN datasets. Quantization can give a false sense of security 4. Lupu. Manuel Barbosa (University of Porto (FCUP) and INESC TEC); Gilles Barthe (MPI-SP and IMDEA Software Institute); Benjamin Grégoire (INRIA Sophia Antipolis); Adrien Koutsos (INRIA Paris); Pierre-Yves Strub (Ecole Polytechnique) LassoNet: Neural Networks with Feature Sparsity. • 3D Brain Midline Delineation for Hematoma Patients. Definitions “1394 Audio Output” means an output that complies with the specification titled “Consumer audio/video equipment - Digital interface - Part 6: Audio and Music Data Transmission Protocol” (IEC … Schaumont, P.R. The Adversarial Robustness Toolbox (ART) is a Python library designed to support researchers and developers in creating novel defence techniques, as well as in deploying practical defences of real-world AI systems. The watermark may be embedded or extracted in both compressed and uncompressed formats. If all words have been altered without changing the target model’s prediction, then the attack has failed (done). Spotlight s 5:15-5:55. CVPR 2017 Masked Layer Distillation: Fast and Robust Training Through Knowledge Transfer Normalization Derek Wan [advisor: Joseph Gonzalez] Model-Agnostic Defense for Lane Detection Against Adversarial Attack Henry Xu [advisor: David A. Wagner] Modin OpenMPI Compute Engine Andrew Zhang [advisor: Randy H. Katz] 3 Adaptive Iterative Attack towards Explainable Adversarial Robustness. A hybrid spatial domain watermarking algorithm that includes encryption, integrity protection, and steganography is proposed to strengthen the information originality based on the authentication. Adding noise to an underconstrained neural network model with a small training dataset can have a regularizing effect and reduce overfitting. The first method is a statistical approach that modified the distribution of vertex norms to hide watermark information into host 3D model while the second method is a mixed insertion of watermark bits into host model using vertex norm distribution and mesh vertices at the same time. The security of patient information is important during the transfer of medical data. Although recent works have analysed the robustness of different compression meth-ods that are typically used for the deployment of DNNs on edge devices against adversarial examples, these have yet to explore robustness against universal attacks in the form of UAPs. Bring your world to life with your own poses, rigs, and renders. We also create more accessible attacks using Universal Adversarial Perturbations which pose a very feasible attack scenario since they can be easily shared amongst attackers. According to the study, Most of the DL methods are fragile and vulnerable against adversarial attacks. The researchers report that even if the robustness of DL based method can be improved by retraining the classifier with adversarial examples, the resulting networks are still vulnerable against most powerful attacks. Abstract efficient encoding and compression of data. Shape. A STUDY ON LIGHT FIELD DENOISING FOR 3D CONSISTENT VISUALIZATION. Hsiao, M.S. Fault Attacks on Cryptosystems: Novel Threat Models, Countermeasures and Evaluation Metrics. Abstract: We present a new method for black-box adversarial attack. attack and then reduce strongly after our attack, even on heavily compressed images. 1. UAPs for Texture vs. Initially, some periodicals might show only one format while others show all three. A TEMPORAL PRE-FILTER FOR VIDEO … Co, L. Muñoz-González, D. Martinez, E.C. 2. In a more practical situation, the attacker will be easily detected because of too many queries, and this problem is especially obvious under the black-box setting. Google Scholar This property of adversarial examples, which is very beneficial for attackers, has been termed transferability (Papernot et al., 2016a ). Abstract: Neural network compression methods like pruning and quantization are very effective at efficiently deploying Deep Neural Networks (DNNs) on edge … Presented at New England Hardware Security Day, April 2021. Recent works have demonstrated that it is possible to spoof LiDAR return signals to elicit fake objects. Conference: 15th Conference on Computer and Robot Vision. “Analog Protection System (APS) Trigger Bits (APSTB)” means the bits as specified (a) for NTSC video signals, in IEC 61880 (for inclusion of such value on Line 20) and EIA-608-B (for inclusion of such value on Line 21) or (b) for YUV (525/60 systems) signals, in IEC 61880 (for inclusion of such value on Line 20) and EIA-608-B (for inclusion of such value on Line 21). ASP-DAC2018: Q. Liu, T. Liu, Z. Liu, Y. Wang, Y. Jin and W. Wen, “Security Analysis and Enhancement of Model Compressed Deep Learning Systems under Adversarial Attacks", Proc. ... instead of making the model robust, consider branching o the main Contribute to 52CV/ICCV-2021-Papers development by creating an account on GitHub. An example is penetration testing, where cybersecurity experts perform different attacks on a system to discover flaws and vulnerabilities. While the watermark is inaudible within its host signal and extremely difficult to remove via unauthorized access, it may be easily … Blackbox attack Transferability of adversaries Gradient estimation 12/41. PrePrints 2022. 2.3 Model Extraction. Adversarial Training Adversarial training [33] uses a min-max robust op-timization formulation to capture the notion of security against adversarial attacks. of Asia and South Pacific Design Automation Conference (ASP-DAC), 2018. Download PDF. The attacker then creates an attack based on the surrogate model, which is likely to still perform well when applied to the targeted model, even if the model classes differ. (NSF SaTC project) [ICLR’21 Workshop] Siyue Wang, Xiao Wang, Pin-Yu Chen, Pu Zhao, and Xue Lin. In this work, the GAN is introduced to the data transmission process along with protection and also it detects an attack during transmission, Here, in between the generator, G, and the discriminator, D, min-max two player’s game is presented. A SYNDROME-BASED AUTOENCODER FOR POINT CLOUD GEOMETRY COMPRESSION. DOI: 10.1109/CRV.2018.00018. Robustness and Transferability of Universal Attacks on Compressed Models LiDARs play a critical role in Autonomous Vehicles’ (AVs) perception and their safe operations. A STUDY OF PREDICTION METHODS BASED ON MACHINE LEARNING TECHNIQUES FOR LOSSLESS IMAGE CODING. Robustness and Transferability of Universal Attacks on Compressed Models. Alberto G. Matachana, Kenneth T. Co, Luis Muñoz-González, David Martínez, and Emil C. Lupu. Optical Illusions. (Best paper nomination)(Acceptance Rate: 28%) Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality Yi Zhang, Orestis Plevrakis, Simon S. Du, Xingguo Li, Zhao Song, Sanjeev Arora. A Real-time Management of Distribution Voltage Fluctuations due to High Solar Photovoltaic (PV) Penetrations. AAAI Workshop: RSEML 2021 – Robustness and Transferability of Universal Attacks on Compressed Models Conclusions 25 1. Examples of 1) include the use of Fokker-Plank equation and Stochastic Differential Equations to understand Stochastic Gradient Descent , the use of Game Theory to provide robustness to trained networks to adversarial attacks or Complexity Theory to understand how Recurrent Networks can achieve super Universal Turing Machine capabilities . This layer can be used to add noise to an existing model. Interspeech 2019 Graz, Austria 15-19 September 2019 Chairs: Gernot Kubin and Zdravko Kačič doi: 10.21437/Interspeech.2019 Text Techniques Text being hidden: “I'm having a great time learning about computer security”. Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving James Tu, Huichen Li, Xinchen Yan, Mengye Ren, Yun Chen, Ming Liang, Eilyan Bitar, Ersin Yumer, Raquel Urtasun Arxiv, 2021 abstract | arXiv. Adversarial attacks against black-box AI models. averaged across all models and attack algorithms. Open Access Link Keras supports the addition of Gaussian noise via a separate layer called the GaussianNoise layer. The proposed algorithm checks whether the patient’s information has been deliberately … The machine learning community recently proposed several federated learning methods that were claimed to be robust against Byzantine failures (e.g., system failures, adversarial manipulations) of certain client devices. On the Effect of Auxiliary Tasks on Representation Dynamics. Meta-Curvature. Projection-Free Optimization on Uniformly Convex Sets. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Shape. High-robustness, low-transferability fingerprinting of neural networks. Two methods were used to embed the watermark into 3D model. Matachana A, Co KT, Munoz Gonzalez L, et al. SFP improves the model’s robustness to transfer attacks 3. Optical Illusions. Federated Machine Learning. 61. CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks Ruchir Puri, David S. Kung, Geert Janssen, Wei Zhang, Giacomo Domeniconi, Vladimir Zolotov, Julian Dolby, Jie Chen, Mihir Choudhury, Lindsey Decker, Veronika Thost, Luca Buratti, Saurabh Pujar, Shyam Ramji, Ulrich Finkler, Susan Malaika, and Frederick Reiss … 2019. We demonstrate that adversarial examples from our attacks are transferable from the unsecured model to the defensively distilled (secured) model. ness and transferability of universal attacks on compressed DNNs through the lens of UAPs. Physical Attack Transferability of Attack Black-box Evasion Attack Jacobian-based Data Augmentation. CAG: A Real-time Low-cost Enhanced-robustness High-transferability Content-aware Adversarial Attack Generator. The Effect of Prior Lipschitz Continuity on the Adversarial Robustness of Bayesian Neural Networks Model-Agnostic Defense for Lane Detection Against Adversarial Attack (EECS-2021-105) Henry Xu, An Ju and David A. Wagner. This paper studies classification models, especially DNN-based ones, to demonstrate that there exists intrinsic relationships between their sparsity and adversarial robustness. The Southern California Machine Learning Symposium brings together students and faculty to promote machine learning in the southern California region. Robustness and Transferability of Universal Attacks on Compressed Models. (acceptance rate 1 3.9 %). Ghosh, Shibani. If you are new to this topic, we suggest running the notebooks for the CIFAR-10 UAPs first. Naomi Sirkin (previously Naomi Ephraim) I am a Ph.D. candidate in the Computer Science department at Cornell University.My advisor is Professor Rafael Pass.My research interests are in the foundations of cryptography.In particular, I've recently been interested in interactive proof systems, program obfuscation, and cryptographic lower bounds. 16/41. In Proc. Shape Robustness and Transferability of Universal Attacks on Compressed Models Presentations , Publications / By Emil Lupu A.G. Matachana, K.T. NeurIPS (2020). • 2.5D Thermometry Maps for MRI-guided Tumor Ablation. in Electrical Engineering from EPFL in 2012. Adversarial Detection and Correction by Matching Prediction Distributions Arno Blaas and Stephen Roberts. Universal Spectral Adversarial Attacks for Deformable Shapes 24 Image-agnostic perturbations are known to exist *. Presented at New England Hardware Security Day, April 2021. Part of evaluating the robustness of all software and computer systems is to test them under duress and attacks. Finally, we consider a scenario where the objective is to fool both the camera model classifier and the GAN detector at the same time. This mail is being sent in compliance with Senate bill 2116 , Title 9 ; Section 303 ! 61. May 2018. INTRODUCTION Adversarial attacks are algorithms that add imperceptible perturba-tions to the input signal of a machine learning model in order to generate an incorrect output. ISBN: 978-1-6654-3864-3. •We propose a more accessible attack on Deepfake detectors using universal adversarial A model extraction attack refers to stealing a target model h θ through black-box access, that is, through posing queries to the model over a predefined interface as depicted in Figure 2B.An attacker might use those queries to h θ to obtain labels for unlabeled data D s′ from distribution D.Given D s′ and the corresponding labels obtained from the original … [IJCAI] Z. Xiao, Y Xie, J. Chen and B. Yuan, "Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models," accepted in International Joint Conferences on Artificial Intelligence (IJCAI), Aug. 2021.

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