Slides from a project the 2018 Brains, Minds, & Machines summer school, on understanding adversarial examples in deep convolutional neural networks, using attention maps.
3. min
~✓
C(~x, y; ~✓)
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Adversarial Examples
During training, we optimize parameters to minimize cost C,
To attack network, we optimize input, , to maximize cost,
Simplest method: Alter each pixel by amount , to increase the cost function
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~x + ~<latexit sha1_base64="EuB5WEOcXmAa71wSIdIQAZPnKcU=">AAAB/XicbVDLSsNAFJ34rPUVHzs3wSIIQklE0GXRjcsK9gFNKJPJTTt0Mgkzk2INwV9x40IRt/6HO//GaZqFth643MM59zJ3jp8wKpVtfxtLyyura+uVjerm1vbOrrm335ZxKgi0SMxi0fWxBEY5tBRVDLqJABz5DDr+6Gbqd8YgJI35vZok4EV4wGlICVZa6puH7hhI9pCfFd0NgCmc982aXbcLWIvEKUkNlWj2zS83iEkaAVeEYSl7jp0oL8NCUcIgr7qphASTER5AT1OOI5BeVlyfWydaCawwFrq4sgr190aGIyknka8nI6yGct6biv95vVSFV15GeZIq4GT2UJgyS8XWNAoroAKIYhNNMBFU32qRIRaYKB1YVYfgzH95kbTP645dd+4uao3rMo4KOkLH6BQ56BI10C1qohYi6BE9o1f0ZjwZL8a78TEbXTLKnQP0B8bnDxkFlaA=</latexit><latexit sha1_base64="EuB5WEOcXmAa71wSIdIQAZPnKcU=">AAAB/XicbVDLSsNAFJ34rPUVHzs3wSIIQklE0GXRjcsK9gFNKJPJTTt0Mgkzk2INwV9x40IRt/6HO//GaZqFth643MM59zJ3jp8wKpVtfxtLyyura+uVjerm1vbOrrm335ZxKgi0SMxi0fWxBEY5tBRVDLqJABz5DDr+6Gbqd8YgJI35vZok4EV4wGlICVZa6puH7hhI9pCfFd0NgCmc982aXbcLWIvEKUkNlWj2zS83iEkaAVeEYSl7jp0oL8NCUcIgr7qphASTER5AT1OOI5BeVlyfWydaCawwFrq4sgr190aGIyknka8nI6yGct6biv95vVSFV15GeZIq4GT2UJgyS8XWNAoroAKIYhNNMBFU32qRIRaYKB1YVYfgzH95kbTP645dd+4uao3rMo4KOkLH6BQ56BI10C1qohYi6BE9o1f0ZjwZL8a78TEbXTLKnQP0B8bnDxkFlaA=</latexit><latexit sha1_base64="EuB5WEOcXmAa71wSIdIQAZPnKcU=">AAAB/XicbVDLSsNAFJ34rPUVHzs3wSIIQklE0GXRjcsK9gFNKJPJTTt0Mgkzk2INwV9x40IRt/6HO//GaZqFth643MM59zJ3jp8wKpVtfxtLyyura+uVjerm1vbOrrm335ZxKgi0SMxi0fWxBEY5tBRVDLqJABz5DDr+6Gbqd8YgJI35vZok4EV4wGlICVZa6puH7hhI9pCfFd0NgCmc982aXbcLWIvEKUkNlWj2zS83iEkaAVeEYSl7jp0oL8NCUcIgr7qphASTER5AT1OOI5BeVlyfWydaCawwFrq4sgr190aGIyknka8nI6yGct6biv95vVSFV15GeZIq4GT2UJgyS8XWNAoroAKIYhNNMBFU32qRIRaYKB1YVYfgzH95kbTP645dd+4uao3rMo4KOkLH6BQ56BI10C1qohYi6BE9o1f0ZjwZL8a78TEbXTLKnQP0B8bnDxkFlaA=</latexit><latexit sha1_base64="EuB5WEOcXmAa71wSIdIQAZPnKcU=">AAAB/XicbVDLSsNAFJ34rPUVHzs3wSIIQklE0GXRjcsK9gFNKJPJTTt0Mgkzk2INwV9x40IRt/6HO//GaZqFth643MM59zJ3jp8wKpVtfxtLyyura+uVjerm1vbOrrm335ZxKgi0SMxi0fWxBEY5tBRVDLqJABz5DDr+6Gbqd8YgJI35vZok4EV4wGlICVZa6puH7hhI9pCfFd0NgCmc982aXbcLWIvEKUkNlWj2zS83iEkaAVeEYSl7jp0oL8NCUcIgr7qphASTER5AT1OOI5BeVlyfWydaCawwFrq4sgr190aGIyknka8nI6yGct6biv95vVSFV15GeZIq4GT2UJgyS8XWNAoroAKIYhNNMBFU32qRIRaYKB1YVYfgzH95kbTP645dd+4uao3rMo4KOkLH6BQ56BI10C1qohYi6BE9o1f0ZjwZL8a78TEbXTLKnQP0B8bnDxkFlaA=</latexit>
max
~
C(~x + ~, y; ~✓)
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for small ~<latexit sha1_base64="lmYqiAAfdewB5VApBlqLn4BR5GU=">AAAB83icbVBNS8NAEJ3Ur1q/qh69LBbBU0lE0GPRi8cK9gOaUDababt0swm7m0IJ/RtePCji1T/jzX/jts1BWx8MPN6bYWZemAqujet+O6WNza3tnfJuZW//4PCoenzS1kmmGLZYIhLVDalGwSW2DDcCu6lCGocCO+H4fu53Jqg0T+STmaYYxHQo+YAzaqzk+xNkuR+hMHTWr9bcursAWSdeQWpQoNmvfvlRwrIYpWGCat3z3NQEOVWGM4Gzip9pTCkb0yH2LJU0Rh3ki5tn5MIqERkkypY0ZKH+nshprPU0Dm1nTM1Ir3pz8T+vl5nBbZBzmWYGJVsuGmSCmITMAyARV8iMmFpCmeL2VsJGVFFmbEwVG4K3+vI6aV/VPbfuPV7XGndFHGU4g3O4BA9uoAEP0IQWMEjhGV7hzcmcF+fd+Vi2lpxi5hT+wPn8AWnJkeo=</latexit><latexit sha1_base64="lmYqiAAfdewB5VApBlqLn4BR5GU=">AAAB83icbVBNS8NAEJ3Ur1q/qh69LBbBU0lE0GPRi8cK9gOaUDababt0swm7m0IJ/RtePCji1T/jzX/jts1BWx8MPN6bYWZemAqujet+O6WNza3tnfJuZW//4PCoenzS1kmmGLZYIhLVDalGwSW2DDcCu6lCGocCO+H4fu53Jqg0T+STmaYYxHQo+YAzaqzk+xNkuR+hMHTWr9bcursAWSdeQWpQoNmvfvlRwrIYpWGCat3z3NQEOVWGM4Gzip9pTCkb0yH2LJU0Rh3ki5tn5MIqERkkypY0ZKH+nshprPU0Dm1nTM1Ir3pz8T+vl5nBbZBzmWYGJVsuGmSCmITMAyARV8iMmFpCmeL2VsJGVFFmbEwVG4K3+vI6aV/VPbfuPV7XGndFHGU4g3O4BA9uoAEP0IQWMEjhGV7hzcmcF+fd+Vi2lpxi5hT+wPn8AWnJkeo=</latexit><latexit sha1_base64="lmYqiAAfdewB5VApBlqLn4BR5GU=">AAAB83icbVBNS8NAEJ3Ur1q/qh69LBbBU0lE0GPRi8cK9gOaUDababt0swm7m0IJ/RtePCji1T/jzX/jts1BWx8MPN6bYWZemAqujet+O6WNza3tnfJuZW//4PCoenzS1kmmGLZYIhLVDalGwSW2DDcCu6lCGocCO+H4fu53Jqg0T+STmaYYxHQo+YAzaqzk+xNkuR+hMHTWr9bcursAWSdeQWpQoNmvfvlRwrIYpWGCat3z3NQEOVWGM4Gzip9pTCkb0yH2LJU0Rh3ki5tn5MIqERkkypY0ZKH+nshprPU0Dm1nTM1Ir3pz8T+vl5nBbZBzmWYGJVsuGmSCmITMAyARV8iMmFpCmeL2VsJGVFFmbEwVG4K3+vI6aV/VPbfuPV7XGndFHGU4g3O4BA9uoAEP0IQWMEjhGV7hzcmcF+fd+Vi2lpxi5hT+wPn8AWnJkeo=</latexit><latexit sha1_base64="lmYqiAAfdewB5VApBlqLn4BR5GU=">AAAB83icbVBNS8NAEJ3Ur1q/qh69LBbBU0lE0GPRi8cK9gOaUDababt0swm7m0IJ/RtePCji1T/jzX/jts1BWx8MPN6bYWZemAqujet+O6WNza3tnfJuZW//4PCoenzS1kmmGLZYIhLVDalGwSW2DDcCu6lCGocCO+H4fu53Jqg0T+STmaYYxHQo+YAzaqzk+xNkuR+hMHTWr9bcursAWSdeQWpQoNmvfvlRwrIYpWGCat3z3NQEOVWGM4Gzip9pTCkb0yH2LJU0Rh3ki5tn5MIqERkkypY0ZKH+nshprPU0Dm1nTM1Ir3pz8T+vl5nBbZBzmWYGJVsuGmSCmITMAyARV8iMmFpCmeL2VsJGVFFmbEwVG4K3+vI6aV/VPbfuPV7XGndFHGU4g3O4BA9uoAEP0IQWMEjhGV7hzcmcF+fd+Vi2lpxi5hT+wPn8AWnJkeo=</latexit>
~xadv = ~x + ✏ ⇥ sign
⇣
r~xC(~x, y; ~✓)
⌘
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Smallness of compensated for by high dimensionality of ~x<latexit sha1_base64="geeElZAuNIASGm3nkH7x/beZoaY=">AAAB7nicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48V7Ae0oWy2k3bpZhN2N8US+iO8eFDEq7/Hm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikpeNUMWyyWMSqE1CNgktsGm4EdhKFNAoEtoPx3dxvT1BpHstHM03Qj+hQ8pAzaqzU7k2QZU+zfrniVt0FyDrxclKBHI1++as3iFkaoTRMUK27npsYP6PKcCZwVuqlGhPKxnSIXUsljVD72eLcGbmwyoCEsbIlDVmovycyGmk9jQLbGVEz0qveXPzP66YmvPEzLpPUoGTLRWEqiInJ/Hcy4AqZEVNLKFPc3krYiCrKjE2oZEPwVl9eJ62rqudWvYfrSv02j6MIZ3AOl+BBDepwDw1oAoMxPMMrvDmJ8+K8Ox/L1oKTz5zCHzifP7Hjj8o=</latexit><latexit sha1_base64="geeElZAuNIASGm3nkH7x/beZoaY=">AAAB7nicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48V7Ae0oWy2k3bpZhN2N8US+iO8eFDEq7/Hm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikpeNUMWyyWMSqE1CNgktsGm4EdhKFNAoEtoPx3dxvT1BpHstHM03Qj+hQ8pAzaqzU7k2QZU+zfrniVt0FyDrxclKBHI1++as3iFkaoTRMUK27npsYP6PKcCZwVuqlGhPKxnSIXUsljVD72eLcGbmwyoCEsbIlDVmovycyGmk9jQLbGVEz0qveXPzP66YmvPEzLpPUoGTLRWEqiInJ/Hcy4AqZEVNLKFPc3krYiCrKjE2oZEPwVl9eJ62rqudWvYfrSv02j6MIZ3AOl+BBDepwDw1oAoMxPMMrvDmJ8+K8Ox/L1oKTz5zCHzifP7Hjj8o=</latexit><latexit sha1_base64="geeElZAuNIASGm3nkH7x/beZoaY=">AAAB7nicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48V7Ae0oWy2k3bpZhN2N8US+iO8eFDEq7/Hm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikpeNUMWyyWMSqE1CNgktsGm4EdhKFNAoEtoPx3dxvT1BpHstHM03Qj+hQ8pAzaqzU7k2QZU+zfrniVt0FyDrxclKBHI1++as3iFkaoTRMUK27npsYP6PKcCZwVuqlGhPKxnSIXUsljVD72eLcGbmwyoCEsbIlDVmovycyGmk9jQLbGVEz0qveXPzP66YmvPEzLpPUoGTLRWEqiInJ/Hcy4AqZEVNLKFPc3krYiCrKjE2oZEPwVl9eJ62rqudWvYfrSv02j6MIZ3AOl+BBDepwDw1oAoMxPMMrvDmJ8+K8Ox/L1oKTz5zCHzifP7Hjj8o=</latexit><latexit sha1_base64="geeElZAuNIASGm3nkH7x/beZoaY=">AAAB7nicbVBNS8NAEJ3Ur1q/qh69LBbBU0lEqMeiF48V7Ae0oWy2k3bpZhN2N8US+iO8eFDEq7/Hm//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikpeNUMWyyWMSqE1CNgktsGm4EdhKFNAoEtoPx3dxvT1BpHstHM03Qj+hQ8pAzaqzU7k2QZU+zfrniVt0FyDrxclKBHI1++as3iFkaoTRMUK27npsYP6PKcCZwVuqlGhPKxnSIXUsljVD72eLcGbmwyoCEsbIlDVmovycyGmk9jQLbGVEz0qveXPzP66YmvPEzLpPUoGTLRWEqiInJ/Hcy4AqZEVNLKFPc3krYiCrKjE2oZEPwVl9eJ62rqudWvYfrSv02j6MIZ3AOl+BBDepwDw1oAoMxPMMrvDmJ8+K8Ox/L1oKTz5zCHzifP7Hjj8o=</latexit>
✏<latexit sha1_base64="ssxxscrscFDuELeuu/+9O0MDmKI=">AAAB73icbVDLSgNBEOyNrxhfUY9eBoPgKeyKoMegF48RzAOSJcxOepMhszPrzKwQQn7CiwdFvPo73vwbJ8keNLGgoajqprsrSgU31ve/vcLa+sbmVnG7tLO7t39QPjxqGpVphg2mhNLtiBoUXGLDciuwnWqkSSSwFY1uZ37rCbXhSj7YcYphQgeSx5xR66R2F1PDhZK9csWv+nOQVRLkpAI56r3yV7evWJagtExQYzqBn9pwQrXlTOC01M0MppSN6AA7jkqaoAkn83un5MwpfRIr7UpaMld/T0xoYsw4iVxnQu3QLHsz8T+vk9n4OpxwmWYWJVssijNBrCKz50mfa2RWjB2hTHN3K2FDqimzLqKSCyFYfnmVNC+qgV8N7i8rtZs8jiKcwCmcQwBXUIM7qEMDGAh4hld48x69F+/d+1i0Frx85hj+wPv8AUzRkCA=</latexit><latexit sha1_base64="ssxxscrscFDuELeuu/+9O0MDmKI=">AAAB73icbVDLSgNBEOyNrxhfUY9eBoPgKeyKoMegF48RzAOSJcxOepMhszPrzKwQQn7CiwdFvPo73vwbJ8keNLGgoajqprsrSgU31ve/vcLa+sbmVnG7tLO7t39QPjxqGpVphg2mhNLtiBoUXGLDciuwnWqkSSSwFY1uZ37rCbXhSj7YcYphQgeSx5xR66R2F1PDhZK9csWv+nOQVRLkpAI56r3yV7evWJagtExQYzqBn9pwQrXlTOC01M0MppSN6AA7jkqaoAkn83un5MwpfRIr7UpaMld/T0xoYsw4iVxnQu3QLHsz8T+vk9n4OpxwmWYWJVssijNBrCKz50mfa2RWjB2hTHN3K2FDqimzLqKSCyFYfnmVNC+qgV8N7i8rtZs8jiKcwCmcQwBXUIM7qEMDGAh4hld48x69F+/d+1i0Frx85hj+wPv8AUzRkCA=</latexit><latexit sha1_base64="ssxxscrscFDuELeuu/+9O0MDmKI=">AAAB73icbVDLSgNBEOyNrxhfUY9eBoPgKeyKoMegF48RzAOSJcxOepMhszPrzKwQQn7CiwdFvPo73vwbJ8keNLGgoajqprsrSgU31ve/vcLa+sbmVnG7tLO7t39QPjxqGpVphg2mhNLtiBoUXGLDciuwnWqkSSSwFY1uZ37rCbXhSj7YcYphQgeSx5xR66R2F1PDhZK9csWv+nOQVRLkpAI56r3yV7evWJagtExQYzqBn9pwQrXlTOC01M0MppSN6AA7jkqaoAkn83un5MwpfRIr7UpaMld/T0xoYsw4iVxnQu3QLHsz8T+vk9n4OpxwmWYWJVssijNBrCKz50mfa2RWjB2hTHN3K2FDqimzLqKSCyFYfnmVNC+qgV8N7i8rtZs8jiKcwCmcQwBXUIM7qEMDGAh4hld48x69F+/d+1i0Frx85hj+wPv8AUzRkCA=</latexit><latexit sha1_base64="ssxxscrscFDuELeuu/+9O0MDmKI=">AAAB73icbVDLSgNBEOyNrxhfUY9eBoPgKeyKoMegF48RzAOSJcxOepMhszPrzKwQQn7CiwdFvPo73vwbJ8keNLGgoajqprsrSgU31ve/vcLa+sbmVnG7tLO7t39QPjxqGpVphg2mhNLtiBoUXGLDciuwnWqkSSSwFY1uZ37rCbXhSj7YcYphQgeSx5xR66R2F1PDhZK9csWv+nOQVRLkpAI56r3yV7evWJagtExQYzqBn9pwQrXlTOC01M0MppSN6AA7jkqaoAkn83un5MwpfRIr7UpaMld/T0xoYsw4iVxnQu3QLHsz8T+vk9n4OpxwmWYWJVssijNBrCKz50mfa2RWjB2hTHN3K2FDqimzLqKSCyFYfnmVNC+qgV8N7i8rtZs8jiKcwCmcQwBXUIM7qEMDGAh4hld48x69F+/d+1i0Frx85hj+wPv8AUzRkCA=</latexit>
How is the adversarial noise chosen?
4. Robustness from Fewer Parameters?
Hypothesis: underparametrized networks are less redundant,
and generalize better to adversarial input
# misclassifications out of 5000 Adversarial misclassifications out of 5000
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# parameters # parameters
MNIST training size
No significant gain in robustness, relative to performance
We trained small, all-convolutional networks of varying size, on MNIST:
5. Visualizing Attention
“Feedback CNN” (Cao et. al. 2018)
What features of an image are they responding to?
…and generate attention maps from
selective feedback connections
feedforward feedback
“zebra”
We study VGG-11 deep CNN with ImageNet (Simonyan et. al. 2014)
We would like to understand how models misinterpret adversarial input
6. Selective Feedback
“Zebra” “Lion” ….
“Feedback CNN” (Cao et. al. 2018)
During backwards pass, prune neurons that do not improve the target score
@ˆyzebra
@zneuron
> 0
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@ˆyzebra
@zneuron
< 0
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…and keep other neurons:
(Attention driven by top-down stimuli)
7. Selective Feedback
“Zebra”
“Feedback CNN” (Cao et. al. 2018)
-+ + +-
During backwards pass, prune neurons that do not improve the target score
@ˆyzebra
@zneuron
> 0
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@ˆyzebra
@zneuron
< 0
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…and keep other neurons:
(Attention driven by top-down stimuli)
8. Selective Feedback
“Zebra”
“Feedback CNN” (Cao et. al. 2018)
+ + + --
During backwards pass, prune neurons that do not improve the target score
@ˆyzebra
@zneuron
> 0
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@ˆyzebra
@zneuron
< 0
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…and keep other neurons:
(Attention driven by top-down stimuli)
9. Selective Feedback
“Zebra”
…leads to fine-grained gradient maps, e.g.
“Feedback CNN” (Cao et. al. 2018)
+ + +--
+ + + --
During backwards pass, prune neurons that do not improve the target score
@ˆyzebra
@zneuron
> 0
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@ˆyzebra
@zneuron
< 0
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…and keep other neurons:
(Attention driven by top-down stimuli)