Reversible Adversarial Examples Ustc-PDF Free Download

Deep Adversarial Learning in NLP There were some successes of GANs in NLP, but not so much comparing to Vision. The scope of Deep Adversarial Learning in NLP includes: Adversarial Examples, Attacks, and Rules Adversarial Training (w. Noise) Adversarial Generation Various other usages in ranking, denoising, & domain adaptation. 12

Additional adversarial attack defense methods (e.g., adversarial training, pruning) and conventional model regularization methods are examined as well. 2. Background and Related Works 2.1. Bit Flip based Adversarial Weight Attack The bit-flip based adversarial weight attack, aka. Bit-Flip Attack (BFA) [17], is an adversarial attack variant

(VADA) improved adversarial feature adaptation using VAT. It generated adversarial examples against only the source classifier and adapted on the target domain [9]. Unlike VADA methods, Transferable Adversarial Training (TAT) adversari-ally generates transferable examples that fit the gap between source and target domain [3].

very similar to weight decay k-NN: adversarial training is prone to overfitting. Takeway: neural nets can actually become more secure than other models. Adversarially trained neural nets have the best empirical success rate on adversarial examples of any machine learning model.

deep learning models were vulnerable to adversarial attacks, learning how to generate adversarial examples has quickly attracted wide research interest. Goodfellow et al. [24] devel-oped a single gradient step method to generate adversarial examples,whichwas known asthefastgradientsign method r-

6-6 Reversible Processes. Q-W. 2. 1. T0. H. H. 6-8 CARNOT PRINCIPLES T TEfficiency of two Heat Engines operating between the same two reservoirs at . L and . H. C1. ηη irreversible reversible C2 . ηη reversible 1 reversible 2 L. 6-9 THE THERMODYNAMIC and TEMPERATURE SCALE : For reversible heat engine operating between T L T H. LL . HH QT QT

a b 0, the heat withdrawn from the hot reservoir is Thus, the efficiency of the reversible Carnot heat cycle with an ideal gas is Heat can never be totally converted to work in a reversible cycle process. Since w cycle,,irreversible w cycle,,reversible irreversible reversible 1. Efficiency of a Reversible Heat Engine

1) Adversarial Input Attack and Defense (CVPR'2019) 2) Adversarial Weight Attack and Defense against DRAM memory bit-flip (USENIX Security'2020, ICCV'2019, CVPR'2020, TPAMI'2021 , DAC'20, DATE'21) 3) Adversarial Weight Attack and Defense against power-plundering circuits caused noise

We study both classical decision trees and state-of-the-art ensemble boosting methods such as XGBoost. We show that, similar to neural networks, tree-based models are also vulnerable to adversarial examples. We propose a novel robust decision tree training frame-work to improve robustness against adversarial examples.

Label: Hummingbird Adversarial Perturbations can be Image Agnostic. . Xie, Cihang, ZhishuaiZhang, YuyinZhou, Song Bai, JianyuWang, Zhou Ren, and Alan L. Yuille. "Improving transferability of adversarial examples with input diversity." InCVPR, 2019. Quantitative Result of

Model used: BiDAF Ensemble (Seo et al., 2016) Robin Jia and Percy Liang. Adversarial Examples for Evaluating Reading Comprehension Systems. EMNLP'17. . -Grammar checker -Language model Perplexity -Paraphrases. Content of this Tutorial Introduction to Adversarial Examples

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Reversible logic is highly useful in nanotechnology, low power design and quantum computing. The paper proposes a power efficient design of an ALU, using Reversible Logic Gates. With power management becoming a critical component for hardware design developers, Reversible Logic can provide a viable alternative towards creating low power

well known that the most efficient cycles are reversible cycles. The Carnot heat engine cycle, which is composed of four reversible processes, is the best known reversible cycle observed by Chambadal P. et al. [4]. But in reality reversible processes require an infinite process time and/or an infinite system area

Efficiency of a Carnot Engine For a reversible cycle the amount of heat transferred is proportional to the temperature of the reservoir H L rev Q Q η 1 H L T T 1 Only true for the reversible case COP of a Reversible Heat Pump and a Reversible Refrigerator L H HP rev Q Q COP 1 1, TL TH 1 1 1 1, H L R rev Q Q COP 1 1 .

(c) Reversible isothermal heat rejection (d) Reversible adiabatic compression It states that of all the heat engine operating between constant source and sink temperature, none has higher efficiency than a reversible engine. The efficiency of a reversible engine is independent of the nature or the amount of the working substance undergoing the .

attacks on input data (e.g., images, textual descriptions, audio) used in multimedia recommender systems (MR). In this work, we examine the consequences of applying targeted adversarial attacks against the product images of a visual-based MR. We propose a novel adversarial attack approach, called Target

Chapter 3 Adversarial Attack Consider a data point x 0 2Rd belonging to class C i.Adversarial attack is a malicious attempt which tries to perturb x 0 to a new data point x such that x is misclassi ed by the classi er. Goodfellow et al. made this

"cheats" to minimize the reconstruction loss instead of learning the correct mapping. 4 Defense techniques 4.1 Adversarial training with noise One approach to defend the model from a self-adversarial attack is to train it to be resistant to the perturbation of nature similar to the o

principle, LiBRe can endow a variety of pre-trained task-dependent DNNs with the ability of defending heteroge-neous adversarial attacks at a low cost. We develop and integrate advanced learning techniques to make LiBRe ap-propriate for adversarial detection. Concretely, we build the few-layer deep ensemble variational and adopt the pre-

Published at ICLR 2021 Workshop on Security and Safety in Machine Learning Systems BRIDGING THE GAP BETWEEN ADVERSARIAL RO- BUSTNESS AND OPTIMIZATION BIAS Fartash Faghri 1 ;2Cristina Vasconcelos 3David J. Fleet Fabian Pedregosa3 Nicolas Le Roux3;4 1University of Toronto 2Vector Institute 3Google Research 4Mila ABSTRACT Adversarial robustness is an open challenge in deep learning, most often .

HDR4CV adversarial illumination dataset HDR4CV: High dynamic range dataset with adversarial illumination for testing computer vision methods Param Hanji, 1,a) Muhammad Z. Alam, b) Nicola Giuliani,2, c) Hu Chen,2, d) and Rafa l K. Mantiuk e) 1)University of Cambridge, UK 2)Huawei Technologies, Germany (Dated: 3 September 2021)

Figure 1. (a) Regional attributions to the adversarial attack. Re-gions with high attributions are important for the decrease of the attacking cost. (b2) Perturbation pixels A and B interact with each other and form a curve to conduct the adversarial attack; (b3) the entire perturbation can be decomposed into several components.

Q2U"(Pz) E z (x;y) Q['( ;z)] Mohammad Mehrabi (USC) Tradeo s in adversarial training ICML 20214/10. Adversarial setup: distributional shift Game between learner and adversary Learner: Access to data generated iid from P z Pick model ( with empircal risk minimization, etc.) Adversary:

non-targeted approach is proposed by Moosavi et al. [24], where an image-agnostic Universal Adversarial Perturba-tion (UAP) is computed and applied to unseen images to cause network misclassification. Adversarial attacks on image retrieval are studied by re-cent work [19, 20, 37] in a non-targeted scenario for CNN-basedapproaches.

Additional Key Words and Phrases: Sketch, Normal Map, Point Hints, Generative Adversarial Network, Wasserstein Distance Authors' addresses: Wanchao Su, City University of Hong Kong, wanchao.su@my.cityu.edu.hk; Dong Du, University of Science and Technology of China, City University of Hong Kong, dongdu@mail.ustc.edu.cn; Xin Yang, Dalian .

Improving Transferability of Adversarial Examples with Input Diversity Cihang Xie1 Zhishuai Zhang1 Yuyin Zhou1 Song Bai2 Jianyu Wang3 Zhou Ren4 Alan Yuille1 1Johns Hopkins University 2University of Oxford 3Baidu Research 4Wormpex AI Research Abstract Though CNNs have achieved the state-of-the-art perfor-

since excessive queries would not be allowed. By contrast, another typical black-box attack, called transfer-based at-tack, relies on the cross-model transferability of adversar-ial examples [21] (i.e., adversarial examples crafted on one model could successfully attack other models for the same task), which is

large provably robust regions including ones containing ˇ10573 adversarial exam-ples for pixel intensity and ˇ10599 for geometric perturbations. The provability enables our robust examples to be significantly more effective against state-of-the-art defenses based on randomized smoothing than the individual attacks used to construct the regions.

circuits [3]. In fact, the design of conventional circuitry heavily relies on estab-lished HDLs such as VHDL or Verilog. For reversible circuit design, a clear trend towards higher levels of abstractions can be seen [4,5]. The proposed approaches employ the reversible computation paradigm with its characteristics as well as re-

is volume preserving, i.e. it maps any given set to another set of the same volume. In our context, this just means the determinant term disappears from the change-of-variables formula (Eqn. 1). All this analysis so far was for a single reversible block. What if we build a reversible network by chai

with a reversible chemical reaction which could be regarded as instantaneous with respect to mass trans- fer. Also analytical solutions for both film and pen- etration theory have been presented for first-order reversible and irreversible reactions (

Reversible reactions (shown with a reversible arrow ) do not go to completion. In a closed system, reversible reactions will instead reach a state known as dynamic equilibrium. - At equilibrium, the rates of the forward and rever

REVERSIBLE OXYGEN BINDING to Mn, Co and Cu PORPHYRINS . Perhaps the most studied reversible binding reaction to metal porphyrin receptors concerns the dioxygen ligand. The binding of O. 2. is the first step in many important processes

Changes in Reversible and Irreversible Processes In order to understand the entropy change in reversible and irreversible processes, we need to understand the concept of entropy first. For a Carnot heat engine working at T 1 and T 2, it has been observed that the heat absorbed (q 2) and heat returned (q 1) are related as given below. 2. 1 2 2

2. Reversible adiabatic expansion from B to C. No heat leaves the system, ΔS 0, the temperature falls from Th to Tc, the temperature of the cold sink. 3. Reversible isothermal compression from C to D. Heat is released to the cold sink; the change in entropy of the system is qc/Tc, qc is negative. 4. Reversible adiabatic compression from D to A.

The thermal efficiency of any heat engine, reversible or irreversible, is given by η th 1 QL QH Then the efficiency of a Carnot engine, or any reversible heat engine, becomes: η th, rev 1 TL TH This relation is often referred to as the Carnot efficiency, since the Carnot heat engine is the best known reversible engine.

10 The ClausiusInequality and the Second Law The Second Law of Thermodynamics è For irreversible heat engines operating between the same T reservoirs as for the Carnot (reversible) engine, Then, Finally, è For both reversible and irreversible heat engines, where equality is for reversible engines.Similarly, the inequality of

Reversible and irreversible processes Reversible process: consists of a sequence of well-de ned equilibrium states during the intermediate stages of the change from initial state ito nal state f. Each such intermediate state is characterized by some intermediate values (p;V;t) Reversible processes can be represented by a graph in the (p;v) plane.