Explore tweets tagged as #AdversarialExample
OpenAttack - An Open-Source Package For Textual Adversarial Attack #Adversarial #AdversarialAttacks #AdversarialExample #Cloning
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Don't know if I'm just really late with this, but I hadn't noticed before now that there actually *is* something that looks distinctly rifle-like in this well known #adversarialexample. Not true of adversarial examples in general, of course.
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My @TensorFlow model is ~94% confident the image is malignant (it is!), but w/a little alteration, it's now ~92% certain that the image is benign. #AdversarialExample #DeepLearning #Melanoma #AI
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Our latest work examines the security implications of so-called privacy preserving #synthetic data. We show how to balance the inherent trade-offs for voice signals using a new tandem framework,🧵.#voicecloning #spoofingattack.#adversarialexample
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Deep dive into adversarial attacks - #adversarialattack #adversarialexample #deeplearning #machinelearning
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BioDecoded Daily Digest | July 20, 2018. #AI #Cancer #Genomics #Neuroscience #FruitFly #DeepLearning #AdversarialExample #ObfuscatedGradients
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資料が圧倒的に良すぎる / Adversarial Examples 分野の動向 （敵対的サンプル発表資料） by @HirokatuKataoka #adversarialexample #aes @SlideShareさんから.
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Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples | ICML 2018. #DeepLearning #AdversarialExample #ObfuscatedGradients
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RT Interpreting Black Box Machine Learning Models with Genetic Algorithms #frauddetection #adversarialexample #geneticalgorithm #explainableai
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テキスト分類器を騙す攻撃（敵対的サンプル）の防御手法。本手法は敵対的サンプルを正常テキストに「修正」することで攻撃を防ぐ。「TextBugger」で生成した敵対的テキストの約80％を正しく修復できたとのこと（平均修正時間は１秒/１敵対的テキスト）。.#adversarialexample.
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Adversarial Examples 分野の動向 （敵対的サンプル発表資料） by @HirokatuKataoka #adversarialexample #aes @SlideShareさんから.
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📊 Attack Categorization: AttackBench provides a unified framework for categorizing adversarial attacks, simplifying the comparison and understanding of different attack strategies. #AttackBench #AdversarialAttacks #AdversarialExample #AISecurity
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まあこの手のAdversarialExampleは大抵それ自体への敵対的学習でｶｳﾝﾀｰされることで無効化されがちではあるけど、いたちごっこで下手にﾛﾊﾞｽﾄ化し続ければ本来の性能は損なわれるし単純に手間も増えるので無意味というわけでもない たぶん.
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我が半年くらい深層学習の研究させられてた頃に画像分類ﾓﾃﾞﾙへの攻撃としてAdversarialExampleというのが出ていたけど、今のGlazeみたいに学習ﾌｪｰｽﾞで阻害するﾓﾃﾞﾙもその部類に入るんですかね.
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Adversarial Examples 分野の動向 （敵対的サンプル発表資料） by @HirokatuKataoka #adversarialexample #aes @SlideShareさんから.
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