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Universal adversarial
perturbations
2018/05/07 Shun Nakamura
https://arxiv.org/pdf/1610.08401.pdf
Introduction
perturbation(s)とは?
https://arxiv.org/pdf/1412.6572.pdfから引用
・画像に加えられても,区別がつかない程度のノイズ
・日本語だと摂動
・人工知能にとって脅威(我々には違いがわからない)
元画像 ノイズ ???
perturbationはなぜ重要か?(モチベーション)
・DNNと人間との「誤認識」の違いを知る
微小ノイズを与えることで異なる挙動を示す
・対ノイズ用のAI
1. 元々AIに備えつけておく(フェールセーフ)
2. 別で「対ノイズ用AI」を作る
→ 外乱のメカニズムの理解は必須
Proposed method
Universal adversarial perturbations
Universalとは?
・あらゆる画像に適用可能
・どんなNNのモデルにも適用可能
Universalの制約条件
元画像 変換後
摂動を小さくする条件
誤認識を引き起こす条件
Algorithm
●
→ピクセルあたりの誤認識率
● 各ピクセルに対して
● Xiにvが足されても,識別ラベルが変
化しない→新たな識別境界の探索を
する
Algorithm
● FGSM        の改良
であるDeepFoolの改良
→決定境界までの距離rの計算に使用
● 識別境界までのベクトルを可能な限
り保ちつつ,Lp-normを制限以下に
する.
https://arxiv.org/pdf/1412.6572.pdf
Result
ILSVRC2012の誤認識率
・検証データ50000枚,
・ξ=2000(L2),10(L∞)→画像ノルムに対してかなり小さくする
・L2,L∞どちらも高誤認識率を達成
・どのニューラルネット でも高誤認識率を達成
ILSVRC2012の検証結果
・最初8枚:ILVRC2012
・残り:mobileで撮影
・データセットにない画像も誤認識
・各NNモデルに対するperturbations
        ↓
NNが同じならperturbationsも等しいのか?
(データセットによらないのか?)
ILSVRC2012の検証結果
・異なるデータセット(シャッフル)に対するgooglenetのperturbations
・マクロでは似た模様,ミクロだと異なるperturbations
Cross-model universality
・VGG-19は53.6%の誤認識率達成
→アーキテクチャが異なっても,ある程度は「universal perturbations?」
・全くNNに学習させていない画像で誤認識させたい!
→VGG-19が今のところ無難
Universal perturbationsを作るNN
誤認識率を検証したNN
データ数依存性
・googlenetで,学習データ数を変更
・ILSVRC(valデータ:50,000)
・含んでいないクラスがあるのにも高誤認識率
(ImageNetは1000クラス)
誤認識傾向の可視化
・Googlenetで検証
・グラフ構造で表現:(正解ラベル →誤ラベル)
・いくつかのラベルにしか誤認識されない
・画像空間において大部分を占めるものが誤認識ラ
ベルとなる(全部で16ラベル)
Summary
● doubly universalなperturbationの生成方法を考案
● 異なる画像,異なるNNモデルに適用可能
● 学習データ数に多いものに誤認識される傾向
● 学習データが多いほど,高誤認識

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Universal adversarial perturbations