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DL2: Training and Querying Neural Networks with Logic Ma Marc - PowerPoint PPT Presentation

May 23, 2023 •848 likes •992 views

DL2: Training and Querying Neural Networks with Logic Ma Marc Fischer er , Mislav Balunovi , Dana Drachsler-Cohen, Timon Gehr, Ce Zhang, Martin Vechev github.com/eth-sri/dl2 differencing neural networks finding inputs that deactivates [Pei

  1. DL2: Training and Querying Neural Networks with Logic Ma Marc Fischer er , Mislav Balunovi ć , Dana Drachsler-Cohen, Timon Gehr, Ce Zhang, Martin Vechev github.com/eth-sri/dl2

  2. differencing neural networks finding inputs that deactivates [Pei et al., 2017] neurons NN 1 dog 0 ? ? NN cat NN dog NN 2 cat finding adversarial examples finding adversarial examples [Szegedy et al., 2013] using a generator [Song et al., 2018] ? ? dog NN dog NN dog GEN ( , "cat")

  4. differencing neural networks finding inputs that deactivates [Pei et al., 2017] neurons find i[32, 32, 3] find i[32, 32, 3] where i in [0, 1], where i in [0, 1], class (NN1(i)) = dog, NN(i).l3[17] = 0, class (NN2(i)) = cat, class (NN(i)) = cat, i – image ! < 2 i – image # < 100 finding adversarial examples finding adversarial examples [Szegedy et al., 2013] using a generator [Song et al., 2018] find i[224, 224, 3] find i[100] where i in [0, 1], where i in [-1, 1], class (NN1(i)) = dog, class (NN(GEN(i, cat))) = dog i – image " < 25 return GEN(i, cat)

  5. differencing neural networks NN 1 dog ? find i[32, 32, 3] where i in [0, 1], class (NN1(i)) = dog, NN 2 cat class (NN2(i)) = cat, i – image ! < 2

  6. differencing neural networks finding inputs that deactivates [Pei et al., 2017] neurons find i[32, 32, 3] find i[32, 32, 3] where i in [0, 1], where i in [0, 1], class (NN1(i)) = dog, NN(i).l3[17] = 0, class (NN2(i)) = cat, class (NN(i)) = cat, i – image ! < 2 i – image # < 100 finding adversarial examples finding adversarial examples [Szegedy et al., 2013] using a generator [Song et al., 2018] find i[224, 224, 3] find i[100] where i in [0, 1], where i in [-1, 1], class (NN1(i)) = dog, class (NN(GEN(i, cat))) = dog i – image " < 25 return GEN(i, cat)

  7. differencing neural networks finding inputs that deactivates [Pei et al., 2017] neurons find i[32, 32, 3] find i[32, 32, 3] where i in [0, 1], where i in [0, 1], class (NN1(i)) = dog, NN(i).l3[17] = 0, class (NN2(i)) = cat, class (NN(i)) = cat, i – image ! < 2, i – image # < 100, NN1(i).p[dog] > 0.8, i[:8, :8, :] = image[:8, :8, :] NN1(i).p[cat] < 0.1 finding adversarial examples finding adversarial examples [Szegedy et al., 2013] using a generator [Song et al., 2018] find i[224, 224, 3] find i[100] where i in [0, 1], where i in [-1, 1], class (NN1(i)) = dog, class (NN1(GEN(i, cat))) = dog, i – image " < 25, class (NN2(GEN(i, cat))) = car i – image " > 5 return GEN(i, cat)

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