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Deep learning systems are known to be vulnerable to adversarial examples.
Finding similar files in a large file system
1994
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Copy detection mechanisms for digital documents
1995
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Scam: A copy detection mechanism for digital documents
1995
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Gradient-based learning applied to document recognition
1998
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A language independent approach for detecting duplicated code
1999
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The Sybil attack
2002
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Approximate object location and spam filtering on peer-to-peer systems
2003
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Automated worm fingerprinting
2004
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Detecting and filtering instant messaging spam-a global and personalized approach
2005
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Natural evolution strategies
2008
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Learning multiple layers of features from tiny images
2009
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Hashing and data fingerprinting in digital forensics
2009
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Comparison and evaluation of code clone detection techniques and tools: A qualitative approach
2009
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Learning word vectors for sentiment analysis
2011
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Face recognition in unconstrained videos with matched background similarity
2011
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What facebook deals with everyday: 2.7 billion likes, 300 million photos uploaded and 500 terabytes of data, 2012
2012
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Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
2012
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Twitter turns six, 2012
2012
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Kind of like that, 2013
2013
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You are how you click: Clickstream analysis for sybil detection
2013
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Explaining and harnessing adversarial examples
2014
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Uncovering social network sybils in the wild
2014
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ImageNet Large Scale Visual Recognition Challenge
2015
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Defensive distillation is not robust to adversarial examples
2016
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Deep residual learning for image recognition
2016
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Identity mappings in deep residual networks
2016
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Adversarial examples in the physical world
2016
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Having multiple online identities is more normal than you think
2016
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Malware clustering using suffix trees
2016
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Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
2016
Cited alongside, same era.
Distillation as a defense to adversarial perturbations against deep neural networks
2016
Cited alongside, same era.
Improving the robustness of deep neural networks via stability training
2016
Cited alongside, same era.
Adversarial examples are not easily detected: Bypassing ten detection methods
2017
Cited alongside, same era.
Magnet and efficient defenses against adversarial attacks are not robust to adversarial examples
2017
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Sign-opt: A query-efficient hard-label adversarial attack
2019
Later among the works it cites.
Improving black-box adversarial attacks with a transfer-based prior
2019
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Evading defenses to transferable adversarial examples by translation-invariant attacks
2019
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Black-box adversarial attack with transferable model-based embedding
2019
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Making targeted black-box evasion attacks effective and efficient
2019
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Prada: protecting against dnn model stealing attacks
2019
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2017
Cited alongside, same era.
Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models
2017
Cited alongside, same era.
Adversarial example defenses: Ensembles of weak defenses are not strong
2017
Cited alongside, same era.
Adversarial machine learning at scale
2017
Cited alongside, same era.
Delving into transferable adversarial examples and black-box attacks
2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
2017
Cited alongside, same era.
Practical black-box attacks against machine learning
2017
Cited alongside, same era.
Textbugger: Generating adversarial text against real-world applications
2019
Later among the works it cites.
Nesterov accelerated gradient and scale invariance for adversarial attacks
2019
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Parsimonious black-box adversarial attacks via efficient combinatorial optimization
2019
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Autozoom: Autoencoder-based zeroth order optimization method for attacking black-box neural networks
2019
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Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
2019
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Improving transferability of adversarial examples with input diversity
2019
Later among the works it cites.
Square attack: a query-efficient black-box adversarial attack via random search
2020
Closest in time.
Hopskipjumpattack: A query-efficient decision-based attack
2020
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Stateful detection of black-box adversarial attacks
2020
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Sparse-rs: a versatile framework for query-efficient sparse black-box adversarial attacks
2020
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Query-efficient physical hard-label attacks on deep learning visual classification
2020
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Perturbing across the feature hierarchy to improve standard and strict blackbox attack transferability
2020
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Is bert really robust? a strong baseline for natural language attack on text classification and entailment
2020
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Qeba: Query-efficient boundary-based blackbox attack
2020
Closest in time.
Surfree: a fast surrogate-free black-box attack
2020
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Hybrid batch attacks: Finding black-box adversarial examples with limited queries
2020
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Fast is better than free: Revisiting adversarial training
2020
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Skip connections matter: On the transferability of adversarial examples generated with resnets
2020
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Filckr.com, Sep 2020
2020
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https://www.microsoft.com/en-us/photodna
Photodna, 2021 · 2021
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Generating natural language attacks in a hard label black box setting
2021
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Policy-driven attack: Learning to query for hard-label black-box adversarial examples
2021
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