Relevant bibliographies by topics / Adversarial Testing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Adversarial Testing'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 February 2022

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Journal articles on the topic "Adversarial Testing"

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Lindley, Dennis V., and Nozer D. Singpurwalla. "Adversarial Life Testing." Journal of the Royal Statistical Society: Series B (Methodological) 55, no. 4 (September 1993): 837–47. http://dx.doi.org/10.1111/j.2517-6161.1993.tb01944.x.

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Rufo, M. J., J. Martín, and C. J. Pérez. "Adversarial life testing: A Bayesian negotiation model." Reliability Engineering & System Safety 131 (November 2014): 118–25. http://dx.doi.org/10.1016/j.ress.2014.06.007.

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Dürr, Christoph, Thomas Erlebach, Nicole Megow, and Julie Meißner. "An Adversarial Model for Scheduling with Testing." Algorithmica 82, no. 12 (July 10, 2020): 3630–75. http://dx.doi.org/10.1007/s00453-020-00742-2.

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Edmond, Gary. "Forensic science and the myth of adversarial testing." Current Issues in Criminal Justice 32, no. 2 (December 1, 2019): 146–79. http://dx.doi.org/10.1080/10345329.2019.1689786.

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Hoque, Endadul, Hyojeong Lee, Rahul Potharaju, Charles Killian, and Cristina Nita-Rotaru. "Automated Adversarial Testing of Unmodified Wireless Routing Implementations." IEEE/ACM Transactions on Networking 24, no. 6 (December 2016): 3369–82. http://dx.doi.org/10.1109/tnet.2016.2520474.

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Liu, Xiaolei, Xiaosong Zhang, Nadra Guizani, Jiazhong Lu, Qingxin Zhu, and Xiaojiang Du. "TLTD: A Testing Framework for Learning-Based IoT Traffic Detection Systems." Sensors 18, no. 8 (August 10, 2018): 2630. http://dx.doi.org/10.3390/s18082630.

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With the popularization of IoT (Internet of Things) devices and the continuous development of machine learning algorithms, learning-based IoT malicious traffic detection technologies have gradually matured. However, learning-based IoT traffic detection models are usually very vulnerable to adversarial samples. There is a great need for an automated testing framework to help security analysts to detect errors in learning-based IoT traffic detection systems. At present, most methods for generating adversarial samples require training parameters of known models and are only applicable to image data. To address the challenge, we propose a testing framework for learning-based IoT traffic detection systems, TLTD. By introducing genetic algorithms and some technical improvements, TLTD can generate adversarial samples for IoT traffic detection systems and can perform a black-box test on the systems.
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CHAN-HON-TONG, Adrien. "An Algorithm for Generating Invisible Data Poisoning Using Adversarial Noise That Breaks Image Classification Deep Learning." Machine Learning and Knowledge Extraction 1, no. 1 (November 9, 2018): 192–204. http://dx.doi.org/10.3390/make1010011.

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Today, the main two security issues for deep learning are data poisoning and adversarial examples. Data poisoning consists of perverting a learning system by manipulating a small subset of the training data, while adversarial examples entail bypassing the system at testing time with low-amplitude manipulation of the testing sample. Unfortunately, data poisoning that is invisible to human eyes can be generated by adding adversarial noise to the training data. The main contribution of this paper includes a successful implementation of such invisible data poisoning using image classification datasets for a deep learning pipeline. This implementation leads to significant classification accuracy gaps.
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Liu, Xiaolei, Xiaojiang Du, Xiaosong Zhang, Qingxin Zhu, Hao Wang, and Mohsen Guizani. "Adversarial Samples on Android Malware Detection Systems for IoT Systems." Sensors 19, no. 4 (February 25, 2019): 974. http://dx.doi.org/10.3390/s19040974.

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Many IoT (Internet of Things) systems run Android systems or Android-like systems. With the continuous development of machine learning algorithms, the learning-based Android malware detection system for IoT devices has gradually increased. However, these learning-based detection models are often vulnerable to adversarial samples. An automated testing framework is needed to help these learning-based malware detection systems for IoT devices perform security analysis. The current methods of generating adversarial samples mostly require training parameters of models and most of the methods are aimed at image data. To solve this problem, we propose a testing framework for learning-based Android malware detection systems (TLAMD) for IoT Devices. The key challenge is how to construct a suitable fitness function to generate an effective adversarial sample without affecting the features of the application. By introducing genetic algorithms and some technical improvements, our test framework can generate adversarial samples for the IoT Android application with a success rate of nearly 100% and can perform black-box testing on the system.
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Zhang, Pengfei, and Xiaoming Ju. "Adversarial Sample Detection with Gaussian Mixture Conditional Generative Adversarial Networks." Mathematical Problems in Engineering 2021 (September 13, 2021): 1–18. http://dx.doi.org/10.1155/2021/8268249.

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It is important to detect adversarial samples in the physical world that are far away from the training data distribution. Some adversarial samples can make a machine learning model generate a highly overconfident distribution in the testing stage. Thus, we proposed a mechanism for detecting adversarial samples based on semisupervised generative adversarial networks (GANs) with an encoder-decoder structure; this mechanism can be applied to any pretrained neural network without changing the network’s structure. The semisupervised GANs also give us insight into the behavior of adversarial samples and their flow through the layers of a deep neural network. In the supervised scenario, the latent feature of the semisupervised GAN and the target network’s logit information are used as the input of the external classifier support vector machine to detect the adversarial samples. In the unsupervised scenario, first, we proposed a one-class classier based on the semisupervised Gaussian mixture conditional generative adversarial network (GM-CGAN) to fit the joint feature information of the normal data, and then, we used a discriminator network to detect normal data and adversarial samples. In both supervised scenarios and unsupervised scenarios, experimental results show that our method outperforms latest methods.
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Bateman, Ian, Daniel Kahneman, Alistair Munro, Chris Starmer, and Robert Sugden. "Testing competing models of loss aversion: an adversarial collaboration." Journal of Public Economics 89, no. 8 (August 2005): 1561–80. http://dx.doi.org/10.1016/j.jpubeco.2004.06.013.

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Dissertations / Theses on the topic "Adversarial Testing"

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McDonough, Kenton Robert. "Torpedo: A Fuzzing Framework for Discovering Adversarial Container Workloads." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/104159.

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Over the last decade, container technology has fundamentally changed the landscape of commercial cloud computing services. In contrast to traditional VM technologies, containers theoretically provide the same process isolation guarantees with less overhead and additionally introduce finer grained options for resource allocation. Cloud providers have widely adopted container based architectures as the standard for multi-tenant hosting services and rely on underlying security guarantees to ensure that adversarial workloads cannot disrupt the activities of coresident containers on a given host. Unfortunately, recent work has shown that the isolation guarantees provided by containers are not absolute. Due to inconsistencies in the way cgroups have been added to the Linux kernel, there exist vulnerabilities that allow containerized processes to generate "out of band" workloads and negatively impact the performance of the entire host without being appropriately charged. Because of the relative complexity of the kernel, discovering these vulnerabilities through traditional static analysis tools may be very challenging. In this work, we present TORPEDO, a set of modifications to the SYZKALLER fuzzing framework that creates containerized workloads and searches for sequences of system calls that break process isolation boundaries. TORPEDO combines traditional code coverage feedback with resource utilization measurements to motivate the generation of "adversarial" programs based on user-defined criteria. Experiments conducted on the default docker runtime runC as well as the virtualized runtime gVisor independently reconfirm several known vulnerabilities and discover interesting new results and bugs, giving us a promising framework to conduct more research.
Master of Science
Over the last decade, container technology has fundamentally changed the landscape of commercial cloud computing services. By abstracting away many of the system details required to deploy software, developers can rapidly prototype, deploy, and take advantage of massive distributed frameworks when deploying new software products. These paradigms are supported with corresponding business models offered by cloud providers, who allocate space on powerful physical hardware among many potentially competing services. Unfortunately, recent work has shown that the isolation guarantees provided by containers are not absolute. Due to inconsistencies in the way containers have been implemented by the Linux kernel, there exist vulnerabilities that allow containerized programs to generate "out of band" workloads and negatively impact the performance of other containers. In general, these vulnerabilities are difficult to identify, but can be very severe. In this work, we present TORPEDO, a set of modifications to the SYZKALLER fuzzing framework that creates containerized workloads and searches for programs that negatively impact other containers. TORPEDO uses a novel technique that combines resource monitoring with code coverage approximations, and initial testing on common container software has revealed new interesting vulnerabilities and bugs.
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Guichard, Jonathan. "Quality Assessment of Conversational Agents : Assessing the Robustness of Conversational Agents to Errors and Lexical Variability." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-226552.

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Assessing a conversational agent’s understanding capabilities is critical, as poor user interactions could seal the agent’s fate at the very beginning of its lifecycle with users abandoning the system. In this thesis we explore the use of paraphrases as a testing tool for conversational agents. Paraphrases, which are different ways of expressing the same intent, are generated based on known working input by performing lexical substitutions and by introducing multiple spelling divergences. As the expected outcome for this newly generated data is known, we can use it to assess the agent’s robustness to language variation and detect potential understanding weaknesses. As demonstrated by a case study, we obtain encouraging results as it appears that this approach can help anticipate potential understanding shortcomings, and that these shortcomings can be addressed by the generated paraphrases.
Att bedöma en konversationsagents språkförståelse är kritiskt, eftersom dåliga användarinteraktioner kan avgöra om agenten blir en framgång eller ett misslyckande redan i början av livscykeln. I denna rapport undersöker vi användningen av parafraser som ett testverktyg för dessa konversationsagenter. Parafraser, vilka är olika sätt att uttrycka samma avsikt, skapas baserat på känd indata genom att utföra lexiska substitutioner och genom att introducera flera stavningsavvikelser. Eftersom det förväntade resultatet för denna indata är känd kan vi använda resultaten för att bedöma agentens robusthet mot språkvariation och upptäcka potentiella förståelssvagheter. Som framgår av en fallstudie får vi uppmuntrande resultat, eftersom detta tillvägagångssätt verkar kunna bidra till att förutse eventuella brister i förståelsen, och dessa brister kan hanteras av de genererade parafraserna.
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Lin, Sheng-Xiang, and 林聖翔. "Automatic Web Security Testing with Generative Adversarial Network." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/tp3858.

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碩士
國立宜蘭大學
資訊工程學系碩士班
107
Assessing software security contain many different types of practices. When you have to perform black box testing, fuzzing test is often used for vulnerability mining. However, there is no way to ensure that the target system has been exploited with all the vulnerabilities unless all the unacceptable inputs of the test target have been tested, but this is not possible. Therefore, it is important to improve the efficiency of testing. In the case of web security, for example, when doing testing, engineers usually prepare a large list of attack vectors. Some well-known free vulnerability scanning tools use a list of out-of-the-box attack vectors, while others generate attack vectors based on a known attack format. Although this approach can save a lot of time and labor costs, it just only test problems that have been identified, and sometimes the success rate is not high. To increase the efficiency of security testing, we're hoping to uncover more vulnerabilities by increasing the variability of attack vectors. Therefore, we proposed an automatic security testing system combining generative adversarial network (GAN). Using generating adversarial networks to generate pseudo-data features, the attack vectors can be learned and generated. We can take advantage of that to make a security engineer have second choice to test the website.
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(9154928), Aritra Mitra. "New Approaches to Distributed State Estimation, Inference and Learning with Extensions to Byzantine-Resilience." Thesis, 2020.

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In this thesis, we focus on the problem of estimating an unknown quantity of interest, when the information required to do so is dispersed over a network of agents. In particular, each agent in the network receives sequential observations generated by the unknown quantity, and the collective goal of the network is to eventually learn this quantity by means of appropriately crafted information diffusion rules. The abstraction described above can be used to model a variety of problems ranging from environmental monitoring of a dynamical process using autonomous robot teams, to statistical inference using a network of processors, to social learning in groups of individuals. The limited information content of each agent, coupled with dynamically changing networks, the possibility of adversarial attacks, and constraints imposed by the communication channels, introduce various unique challenges in addressing such problems. We contribute towards systematically resolving some of these challenges.

In the first part of this thesis, we focus on tracking the state of a dynamical process, and develop a distributed observer for the most general class of LTI systems, linear measurement models, and time-invariant graphs. To do so, we introduce the notion of a multi-sensor observable decomposition - a generalization of the Kalman observable canonical decomposition for a single sensor. We then consider a scenario where certain agents in the network are compromised based on the classical Byzantine adversary model. For this worst-case adversarial setting, we identify certain fundamental necessary conditions that are a blend of system- and network-theoretic requirements. We then develop an attack-resilient, provably-correct, fully distributed state estimation algorithm. Finally, by drawing connections to the concept of age-of-information for characterizing information freshness, we show how our framework can be extended to handle a broad class of time-varying graphs. Notably, in each of the cases above, our proposed algorithms guarantee exponential convergence at any desired convergence rate.

In the second part of the thesis, we turn our attention to the problem of distributed hypothesis testing/inference, where each agent receives a stream of stochastic signals generated by an unknown static state that belongs to a finite set of hypotheses. To enable each agent to uniquely identify the true state, we develop a novel distributed learning rule that employs a min-protocol for data-aggregation, as opposed to the large body of existing techniques that rely on "belief-averaging". We establish consistency of our rule under minimal requirements on the observation model and the network structure, and prove that it guarantees exponentially fast convergence to the truth with probability 1. Most importantly, we establish that the learning rate of our algorithm is network-independent, and a strict improvement over all existing approaches. We also develop a simple variant of our learning algorithm that can account for misbehaving agents. As the final contribution of this work, we develop communication-efficient rules for distributed hypothesis testing. Specifically, we draw on ideas from event-triggered control to reduce the number of communication rounds, and employ an adaptive quantization scheme that guarantees exponentially fast learning almost surely, even when just 1 bit is used to encode each hypothesis.
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Book chapters on the topic "Adversarial Testing"

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Shetty, Rakshith, Mario Fritz, and Bernt Schiele. "Towards Automated Testing and Robustification by Semantic Adversarial Data Generation." In Computer Vision – ECCV 2020, 489–506. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58536-5_29.

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Raj, Sunny, Laura Pullum, Arvind Ramanathan, and Sumit Kumar Jha. "$$\mathcal {SATYA}$$ : Defending Against Adversarial Attacks Using Statistical Hypothesis Testing." In Foundations and Practice of Security, 277–92. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75650-9_18.

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Li, Zuxing, Yang You, and Tobias J. Oechtering. "Privacy Against Adversarial Hypothesis Testing: Theory and Application to Smart Meter Privacy Problem." In Privacy in Dynamical Systems, 43–64. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0493-8_3.

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Pavanetto, Silvio, and Marco Brambilla. "Generation of Realistic Navigation Paths for Web Site Testing Using Recurrent Neural Networks and Generative Adversarial Neural Networks." In Lecture Notes in Computer Science, 244–58. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50578-3_17.

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Melnyk, Virginia Ellyn. "Punch Card Patterns Designed with GAN." In Proceedings of the 2021 DigitalFUTURES, 69–79. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5983-6_7.

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AbstractKnitting punch cards codify different stitch patterns into binary patterns, telling the machine when to change color or to generate different stitch types. This research utilizes Neural Networks (NN) and image-based Generative Adversarial Networks (GAN), with an image database of knitting punch cards, to generate new punch card designs. The hypothesis is that artificial intelligence will learn the basic underlying structures of the punch cards and the pattern makeup that is inherent across patterns of different styles and cultures. Different neural networks were utilized throughout the research, such as Neural Style Transfer (NST), AdaIN Style Transfers, and StyleGAN2. The results from these explorations offer different insights into pattern design and various outcomes of the different neural networks. Ultimately physically testing these punch card designs, these patterns were knit on a domestic knitting machine, resulting in novel fabrication and design techniques that are both digital and craft-based.
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Prosvetov, A. V. "Using the Generative Adversarial Network to Generate Recommendations." In Fuzzy Systems and Data Mining VI. IOS Press, 2020. http://dx.doi.org/10.3233/faia200680.

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Widely used recommendation systems do not meet all industry requirements, so the search for more advanced methods for creating recommendations continues. The proposed new methods based on Generative Adversarial Networks (GAN) have a theoretical comparison with other recommendation algorithms; however, real-world comparisons are needed to introduce new methods in the industry. In our work, we compare recommendations from the Generative Adversarial Network with recommendation from the Deep Semantic Similarity Model (DSSM) on real-world case of airflight tickets. We found a way to train the GAN so that users receive appropriate recommendations, and during A/B testing, we noted that the GAN-based recommendation system can successfully compete with other neural networks in generating recommendations. One of the advantages of the proposed approach is that the GAN training process avoids a negative sampling, which causes a number of distortions in the final ratings of recommendations. Due to the ability of the GAN to generate new objects from the distribution of the training set, we assume that the Conditional GAN is able to solve the cold start problem.
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Pezzat, Michel, Hector Perez-Meana, Toru Nakashika, and Mariko Nakano. "Many-to-Many Symbolic Multi-Track Music Genre Transfer." In Knowledge Innovation Through Intelligent Software Methodologies, Tools and Techniques. IOS Press, 2020. http://dx.doi.org/10.3233/faia200572.

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This paper shows the feasibility of a variant of the Generative Adversarial Network (GAN), called Star GAN, for music genre transfer. This method is noteworthy in that it simultaneously learns many-to-many mappings across different attribute domains using a single generator network. A similar architecture to research in MuseGAN and CycleGAN is applied. Also, as in MGTGAN, Desert Camel MIDI dataset is use for training and testing.
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Saks, Michael J., and Barbara A. Spellman. "Introduction." In The Psychological Foundations of Evidence Law. NYU Press, 2016. http://dx.doi.org/10.18574/nyu/9781479880041.003.0001.

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Rules of evidence are designed to facilitate trials by controlling what evidence may be presented at trial. Those rules came into being, and evolved over time, due to changes in trial process and structure – especially following the rise of adversarial procedure, whereby control over the marshaling and presentation of facts shifted from judges to lawyers. Refinements and reforms continue to be undertaken to try to improve the scope and clarity of the rules. Trial judges must not only apply the rules, they also have the discretion to make rulings in light of their expectations of the impact they think the evidence will have on jurors. This task involves metacognition: one human trying to estimate the thought processes of others. Thus, evidence rulemakers have been and are, effectively, applied psychologists. The rules of evidence reflect their understanding of the psychological processes affecting, and capabilities and limitations of witnesses, lawyers and jurors. Psychological research and methods provide an additional source of insight and assistance in that endeavor. Better psychological understanding should lead to more effective rules. Psychologists typically employ the scientific method: empirically testing hypotheses in an effort to discover which are valid understandings of how people perceive, store, and retrieve information. To evaluate evidence rules, one could conduct experiments directly on a rule, or borrow from existing knowledge developed in basic research and see whether those understandings support existing or proposed evidence rules.
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Postal, Karen. "Relationship with Other Experts." In Testimony That Sticks, 362–77. Oxford University Press, 2019. http://dx.doi.org/10.1093/med-psych/9780190467395.003.0016.

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It is the nature of our adversarial court system that two experts will testify that they have come to different conclusions about the same person. How that fundamental disagreement is handled by the expert determines whether jurors must witness a “pissing contest” or are thoughtfully educated about the nature of the disagreement. Depending on the litigation strategy, one or both attorneys may want to incite such a contest. This chapter provides rationale and strategies from seasoned forensic psychologists and neuropsychologists as well as attorneys and judges for avoiding unproductive conflicts while accurately and productively explaining differences in opinions. Experts, attorneys, and judges all agreed that tearing down another expert’s credibility damages your own on the stand.
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Wurster, Charles F. "DDT Goes to Trial, Finally, in Washington, DC." In DDT Wars. Oxford University Press, 2015. http://dx.doi.org/10.1093/oso/9780190219413.003.0015.

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EPA was only five weeks old on January 7, 1971, when the Court of Appeals ordered the agency to cancel all DDT registrations. The situation was fluid, to say the least. EPA did not know how the cancellation process was to be carried out, since USDA before them had never executed a cancellation procedure. There was no precedent to follow, and the parties did not agree on the rules for cancellation. The cancellation process for DDT clearly would be adversarial, with the pesticide industry already objecting. Represented by Bill Butler, EDF insisted on judicial rules of evidence with qualification of expert witnesses, testimony to be relevant to the topics at issue, and full rights of cross-examination for all parties. That was a bottom line for EDF, and EPA lawyers agreed. If a witness was qualified as expert on topic A and not on topic B, he or she could testify on A but not on B. We had learned from experience that we did not want industry representatives and salesmen or lobbyists making opinion statements and then walking away, leaving a muddy record that would be little more than a popular vote open to varied interpretations. Industry wanted that. EDF wanted competent scientists to build an accurate record, and after months of haggling, EDF and EPA ultimately prevailed. There would be judicial rules of evidence. It was a triumph for Bill Butler and EDF. Little did we know then that this procedure would influence pesticide regulation by EPA far into the future. Judicial rules of evidence proved critically important in the litigation and eventual banning of aldrin, dieldrin, chlordane, heptachlor, and mirex, as we will describe in Chapter 12. Qualified scientists and experts testified in those proceedings, and some previously vocal advocates never appeared. Since EPA had been ordered by the court to consider cancellation of all registrations of DDT, it was the DDT proponents who were bringing the appeal. They were known as the Group Petitioners. It had become the burden of industry to prove DDT safe, whereas before it had been our burden to prove hazard.
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Conference papers on the topic "Adversarial Testing"

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Hoque, Md Endadul, Hyojeong Lee, Rahul Potharaju, Charles E. Killian, and Cristina Nita-Rotaru. "Adversarial testing of wireless routing implementations." In the sixth ACM conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2462096.2462120.

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Barni, Mauro, and Benedetta Tondi. "Multiple-observation hypothesis testing under adversarial conditions." In 2013 IEEE International Workshop on Information Forensics and Security (WIFS). IEEE, 2013. http://dx.doi.org/10.1109/wifs.2013.6707800.

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McNeil, Martha, and Thomas Llanso. "An Analysis of Adversarial Cyber Testing Practice." In 2020 IEEE Systems Security Symposium (SSS). IEEE, 2020. http://dx.doi.org/10.1109/sss47320.2020.9174237.

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Zhang, Peixin, Jingyi Wang, Jun Sun, Guoliang Dong, Xinyu Wang, Xingen Wang, Jin Song Dong, and Ting Dai. "White-box fairness testing through adversarial sampling." In ICSE '20: 42nd International Conference on Software Engineering. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3377811.3380331.

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Li, Zuxing, Tobias J. Oechtering, and Deniz Gunduz. "Smart meter privacy based on adversarial hypothesis testing." In 2017 IEEE International Symposium on Information Theory (ISIT). IEEE, 2017. http://dx.doi.org/10.1109/isit.2017.8006633.

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Guo, Xiujing. "Towards Automated Software Testing with Generative Adversarial Networks." In 2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume (DSN-S). IEEE, 2021. http://dx.doi.org/10.1109/dsn-s52858.2021.00021.

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Zhang, Pengcheng, Qiyin Dai, and Shunhui Ji. "Condition-Guided Adversarial Generative Testing for Deep Learning Systems." In 2019 IEEE International Conference On Artificial Intelligence Testing (AITest). IEEE, 2019. http://dx.doi.org/10.1109/aitest.2019.000-5.

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"Dropout in Testing Phase Makes Adversarial Samples Generation Difficult." In 2019 the 9th International Workshop on Computer Science and Engineering. WCSE, 2019. http://dx.doi.org/10.18178/wcse.2019.06.017.

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Kang, Qiao, Jiarong Xing, Yiming Qiu, and Ang Chen. "Probabilistic profiling of stateful data planes for adversarial testing." In ASPLOS '21: 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3445814.3446764.

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Park, Hyejin, Taaha Waseem, Wen Qi Teo, Ying Hwei Low, Mei Kuan Lim, and Chun Yong Chong. "Robustness Evaluation of Stacked Generative Adversarial Networks using Metamorphic Testing." In 2021 IEEE/ACM 6th International Workshop on Metamorphic Testing (MET). IEEE, 2021. http://dx.doi.org/10.1109/met52542.2021.00008.

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Reports on the topic "Adversarial Testing"

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Raj, Sunny, Sumit Kumar Jha, Laura L. Pullum, and Arvind Ramanathan. Statistical Hypothesis Testing using CNN Features for Synthesis of Adversarial Counterexamples to Human and Object Detection Vision Systems. Office of Scientific and Technical Information (OSTI), May 2017. http://dx.doi.org/10.2172/1361358.

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