Watsonダマされる！？〜AIが生成した画像 vs 各社の画像認識〜

1. Watson AI vs

2. -0.965659 http://www.lr.pi.titech.ac.jp/~takamura/pndic_ja.html

3. 詳細な評価基準は、別な機会に。

4. : aps-smp.wav ( ) Watson Google Microsoft

5. 評価: 音声学に関する学会発表時の音声データを用いた評価今回：2018/11実施前回：2017/12実施 G W A 良質なデータなので、これくらいの精度は出ないとね・・・

6. サービス正解誤り単語認識率単語正解率脱落置換挿入 (Correct) （Accuracy) 1 Watson 110 1 14 2 88.00 86.40 2 Google 120 0 7 0 94.49 96.00 3 Azure 82 15 25 5 67.21 61.60 サービス正解誤り単語認識率単語正解率脱落置換挿入 (Correct) （Accuracy) 1 Watson 99 12 14 9 79.2 72.0 2 Google 111 6 8 2 88.8 87.2 3 Azure 86 13 25 3 69.35 66.4 2018/11 2017/12 ( ) Watson +8.8 +14.4 Google +5.69 +8.8 Azure -2.14 -4.8 W G A : Watson 精度の変化

7. GAN vs (Watson, Azure, Google, Amazon)

8. GOAL 知るー

9. https://bijutsutecho.com/magazine/news/headline/18719

10. ゙゙ GAN DCGAN(Deep Convolutional Generative Adversarial Networks)

11. GAN Generative Adversal Networks (Facebook AI )

12. IN OUT

13. ホンモノ偽札偽造者警察ニセ札鮮

14. Generator ( ) Discrimnator ( ) Discrimnator Generator

15. data x (Discriminator) data z =0 =1 Generator

16. data x =0.6 =0.4 ( 0.6, 0.4) 1 0.4 Discriminator

17. (Generator) Generator data z Generatordata x

18. data z =0.3 =0.7 Discriminator ( ) Generator Discriminator Discriminator data z 本物と認識させるため、その誤差を埋めるパラメータ設定を学習によって獲得する。 Generatorが目指している Discriminatorの判定本物画像=1.0 生成画像=0.0

19. http://www.afpbb.com/articles/-/3194763?pid=20640893 min 𝐺 max 𝐷 𝑉(𝐷, 𝐺) = 𝔼 𝑥~𝑝 𝑑𝑎𝑡𝑎(𝑥) log 𝐷(𝑥) + 𝔼 𝑧~𝑝 𝑧(𝑍) log(1 − 𝐷 𝐺 𝑧 ) ゙゙

20. DCGAN(Deep Convolutional Generative Adversarial Networks) 512 256 128 3 100 z 4 4 8 8 16 16 32 32 conv1 conv2 conv3 G(z) stride 2 stride 2 stride 2 stride 2

21. 10002000300040005000100002500030000

22. START100020005000600070008000900010000

23. Watson giant panda 0.97 giant panda 0.93 giant panda 0.94 giant panda 0.99 giant panda 0.50 Azure animal 0.99 giant panda 0.70 panda 0.36 animal 0.99 giant panda 0.42 panda 0.42 animal 0.99 giant panda 0.42 panda 0.42 animal 0.99 panda 0.56 giant panda 0.09 animal 0.99 giant panda 0.22 panda 0.21 Google Nose 90 Terrestrial animal 74 Green 91 Head 88 SnapShot 83 Terrestrial animal 74 Bear 57 Terrestrial animal 91 Terrestrial animal 94 Amazon Soccer 99.3 giant panda 0.9 8.2 Team Sport 97.6 Soccer 97.6 animal 97.8 giant panda 97.8 Soccer ball 99.9 Watson animal 0.93 giant panda 0.90 Azure animal 0.99 giant panda 0.70 panda 0.36 Google Terrestrial animal 74 Bear 94 Amazon Giant Panda 97.1 本物画像生成画像の識別

24. START1000200050006000700080009000100002500030000400006000080000100000

25. giant panda 0.94 Azure age 33 Female age 33 Male age 32 Female Google Joy 96% Joy 87% Joy 62% Amazon Head 99.4 Art 95.8 Art 84.2 Game 98.2 Chess 98.2 Art 96.2 生成画像の識別 google

26. Watson plant 0.91 barbados cherry 0.80 plant 0.80 vegetable 0.80 plum tomato 0.71 plant 0.84 cranberry bush 0. 60 plum tomato 0.71 nutrition( ) food 0.83 staffed tomato 0.83 Azure flower 0.20 berry 0.16 tomato 0.041 vegetable 0. 78 fruit 0.71 tomato 0.51 fruito,62 Art 0.11 strawberry 0.11 tomato 0.39 food 0.09 vegetable 0.06 Google Food 85 Plant 77 Tomato 55 Natural Foods 95 Food 86 Tomato 71 plant 59 Food 92 Natural Foods 87 Tomato 73 Amazon Produce 99.3 99.2 Food 99.2 plant 99.4 Food 95.3 Produce 95.3 plant 99.4 Food 97.3 Fruit 97.3 plant 99.4 Fruit 91.9 Food 91.9 barbados cherry staffed tomato 生成画像の識別 plum tomato

27. トマトの学習過程

28. animal 0.88 mammal( ) 0.82 marten cat( ) 0.75 Dandie Dinmont dog 1.0 terrier dog 1.0 dod 1.0 guitar 0.6 uke(guiter) 0.60 animal 0.60 animal 0.88 dog 0.71 spaniel dog 0.64 Azure animal 0.80 mammal 0.76 dog 0.64 mammal 0.81 dog 0.68 puppy 0.07 animal 0.40 dog 0.15 animal 0.96 mammal 0.93 dog 0.63 hay 0.57 Google Chow Chow 84 Nose 84 Candiae( ) 82 Canidae( ) 90 Dog 88 Nose 87 Dog Breed 84 Animal 91 Canidae 90 Snout 83 Norfolk Terrier 76 Snout 83 Sporting Group 62 Border Terrier 60 Norfolk Terrier 53 Amazon Animal 87.9 Mammal 81.9 Wildlife( ) 66.8 Lesser Panda 66.8 Bear 66.8 Toy 82.9 Plush( ) 67.3 Mammal 59.8 Dog 59.8 Animal 86 Pet 80.8 Dog 58.7 Mammal 96.2 Animal 96.2 Dog 82.9 spaniel dogDandie Dinmont dogChow Chowmarten cat( )

29. Watson harbor 0.66 shelter 0.66 natural elevation 0.60 nature 0.53 building 0.59 cornice(building) 0.58 ribbonfish 0.57 sky 0.70 building 0.66 ziggurat 0.66 (ancient templr) animal 0.79 fish 0.72 aquatic vertebrate 0.72 ( ) Azure landscape 0.09 sky 0.06 painting 0.05 animal 0.96 mammal 0.65 giraffe 0.10 rock 0.24 shore 0.15 beach 0.15 animal 0.90 winter 0.21 art 0.15 Google Motor Vehicle 90 Mode Of Transport 81 Vehicle 80 Geological Phenomenon 80 ( ) Tree 75 Sky 89 Architecture 65 Terrain( ) 60 Tree 89 Wood 65 Rock 54 Amazon Transportation 97.9 Vehicle 97.9 Truck 97.9 Outdoors 90.5 Nature 87.2 Landscape 87.2 Panoramic 87.2 Outdoors 87.2 Building 99.2 Architecture 99.2 temple 91.6 Outdoors 97.7 Landscape 97.7 Panoramic 97.7 Building 93 生成画像の識別 Watson building 0.85 synagogue( ) 0.72 religious residence( ) 0. Azure sky 0.99 building 0.82 root 0.40 Google Nagoya Castle 83 Amazon Architecture 99,7 Building 99.7 Castle 99.3 本物画像、名古屋城 google

30. THANKS! Any questions?

Watsonダマされる！？ 〜AIが生成した画像 vs 各社の画像認識〜