Code and data for the paper: Targeted Nonlinear Adversarial Perturbations in Images and Videos
Learn (convnet-generated) adversarial perturbations for a single image or video:
where X = original image or video, P = perturbation generator (a convnet), X' = perturbed image or video, M = image or video classification model, y' = output of model M with input X', loss = the loss used for backpropagation. The backpropagated loss was used to update the parameters of model P, and the process was repeated for a number of epochs.
Below is the code used to run the perturbations on images and video alluded to in the paper.
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from img_perts import perturbate
file_name = "dog"
image_path = "data/image_samples/{}.jpg".format(file_name)
save_to_dir = "data/perturbations/{}".format(file_name)
perturbate.main(
image_path=image_path,
kernel_size=3,
nblocks=3,
nlayers=3,
epochs=2000,#000,
lr=0.001,
l1_coeff=1.0,
class_index="max",
init_pert_model=True,
save_to_dir=save_to_dir,
datetime_subdir=True,
)
100%|██████████| 2000/2000 [07:35<00:00, 4.39it/s]
original score: tensor([ 0.8234, 0.1412, 0.0236, 0.0031, 0.0016], device='cuda:0')
perturbed score: tensor(1.00000e-04 *
[ 3.3763, 0.3358, 0.0243, 0.0665, 0.6410], device='cuda:0')
original input evaluation:
5 top classes identified by the model:
(class index) class description: model score
(222) kuvasz: 0.823425173759
(257) Great Pyrenees: 0.141241371632
(207) golden retriever: 0.0236044339836
(208) Labrador retriever: 0.00311248097569
(229) Old English sheepdog, bobtail: 0.00158786645625
perturbated input evaluation:
5 top classes identified by the model:
(class index) class description: model score
(87) African grey, African gray, Psittacus erithacus: 0.996096909046
(676) muzzle: 0.000698636402376
(222) kuvasz: 0.000337631383445
(251) dalmatian, coach dog, carriage dog: 0.000262596004177
(840) swab, swob, mop: 0.000200480382773
results saved to:
original image saved to data/perturbations/dog/2018-07-22 15:39:08.328737/original.jpg
perturbated image saved to data/perturbations/dog/2018-07-22 15:39:08.328737/perturbated.jpg
differences image saved to data/perturbations/dog/2018-07-22 15:39:08.328737/differences.jpg
scores saved to: data/perturbations/dog/2018-07-22 15:39:08.328737/scores.csv
perturbation generator model saved to: data/perturbations/dog/2018-07-22 15:39:08.328737/pert_model.pt
loss history saved to: data/perturbations/dog/2018-07-22 15:39:08.328737/loss_history
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from img_perts import perturbate
file_name = "cat"
image_path = "data/image_samples/{}.jpg".format(file_name)
save_to_dir = "data/perturbations/{}".format(file_name)
perturbate.main(
image_path=image_path,
kernel_size=3,
nblocks=3,
nlayers=3,
epochs=2000,#000,
lr=0.001,
l1_coeff=1.0,
class_index="max",
init_pert_model=True,
save_to_dir=save_to_dir,
datetime_subdir=True,
)
100%|██████████| 2000/2000 [07:43<00:00, 4.31it/s]
img_perts/deep_explanation.py:218: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
img_perts/deep_explanation.py:219: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
def load_input(image_path, graph=False):
original score: tensor([ 0.8022, 0.1246, 0.0701, 0.0011, 0.0004], device='cuda:0')
perturbed score: tensor(1.00000e-04 *
[ 1.4497, 0.3377, 3.5105, 0.2533, 0.1127], device='cuda:0')
original input evaluation:
img_perts/deep_explanation.py:366: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
return outputs
5 top classes identified by the model:
(class index) class description: model score
(281) tabby, tabby cat: 0.802203714848
(282) tiger cat: 0.124585837126
(285) Egyptian cat: 0.0701399073005
(283) Persian cat: 0.00106593884993
(539) doormat, welcome mat: 0.000402584497351
perturbated input evaluation:
5 top classes identified by the model:
(class index) class description: model score
(145) king penguin, Aptenodytes patagonica: 0.987671971321
(83) prairie chicken, prairie grouse, prairie fowl: 0.00900631211698
(331) hare: 0.000443105003797
(285) Egyptian cat: 0.000351052818587
(146) albatross, mollymawk: 0.000272949255304
results saved to:
original image saved to data/perturbations/cat/2018-07-22 15:23:36.936765/original.jpg
perturbated image saved to data/perturbations/cat/2018-07-22 15:23:36.936765/perturbated.jpg
differences image saved to data/perturbations/cat/2018-07-22 15:23:36.936765/differences.jpg
scores saved to: data/perturbations/cat/2018-07-22 15:23:36.936765/scores.csv
perturbation generator model saved to: data/perturbations/cat/2018-07-22 15:23:36.936765/pert_model.pt
loss history saved to: data/perturbations/cat/2018-07-22 15:23:36.936765/loss_history
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from img_perts import perturbate
file_name = "cat2"
image_path = "data/image_samples/{}.jpg".format(file_name)
save_to_dir = "data/perturbations/{}".format(file_name)
perturbate.main(
image_path=image_path,
kernel_size=3,
nblocks=3,
nlayers=3,
epochs=2000,#000,
lr=0.001,
l1_coeff=1.0,
class_index="max",
init_pert_model=True,
save_to_dir=save_to_dir,
datetime_subdir=True,
)
100%|██████████| 2000/2000 [07:34<00:00, 4.40it/s]
img_perts/deep_explanation.py:223: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
plt.figure("original image")
img_perts/deep_explanation.py:224: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
plt.imshow(img)
original score: tensor([ 0.5098, 0.2082, 0.0819, 0.0631, 0.0317], device='cuda:0')
perturbed score: tensor(1.00000e-03 *
[ 3.4428, 1.6645, 0.5694, 3.4967, 1.7787], device='cuda:0')
original input evaluation:
img_perts/deep_explanation.py:371: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
5 top classes identified by the model:
(class index) class description: model score
(285) Egyptian cat: 0.50977897644
(281) tabby, tabby cat: 0.208158865571
(282) tiger cat: 0.0818989351392
(904) window screen: 0.0630929172039
(905) window shade: 0.0317366085947
perturbated input evaluation:
5 top classes identified by the model:
(class index) class description: model score
(782) screen, CRT screen: 0.368403911591
(664) monitor: 0.324531376362
(851) television, television system: 0.176783695817
(508) computer keyboard, keypad: 0.0432575643063
(681) notebook, notebook computer: 0.0202351231128
results saved to:
original image saved to data/perturbations/cat2/2018-07-22 15:31:22.377249/original.jpg
perturbated image saved to data/perturbations/cat2/2018-07-22 15:31:22.377249/perturbated.jpg
differences image saved to data/perturbations/cat2/2018-07-22 15:31:22.377249/differences.jpg
scores saved to: data/perturbations/cat2/2018-07-22 15:31:22.377249/scores.csv
perturbation generator model saved to: data/perturbations/cat2/2018-07-22 15:31:22.377249/pert_model.pt
loss history saved to: data/perturbations/cat2/2018-07-22 15:31:22.377249/loss_history
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from img_perts import perturbate
file_name = "car"
image_path = "data/image_samples/{}.jpg".format(file_name)
save_to_dir = "data/perturbations/{}".format(file_name)
perturbate.main(
image_path=image_path,
kernel_size=3,
nblocks=3,
nlayers=3,
epochs=2000,#000,
lr=0.001,
l1_coeff=1.0,
class_index="max",
init_pert_model=True,
save_to_dir=save_to_dir,
datetime_subdir=True,
)
100%|██████████| 2000/2000 [07:42<00:00, 4.33it/s]
img_perts/deep_explanation.py:213: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
print "original score: {}".format(torch.nn.Softmax()(vgg_model(img))[0, categories])
img_perts/deep_explanation.py:214: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
print "perturbed score: {}".format(torch.nn.Softmax()(vgg_model(pert_img))[0, categories])
original score: tensor([ 0.5307, 0.1315, 0.1093, 0.1037, 0.0631], device='cuda:0')
perturbed score: tensor(1.00000e-03 *
[ 2.6458, 2.2830, 0.5999, 0.5904, 0.2433], device='cuda:0')
original input evaluation:
img_perts/deep_explanation.py:361: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
outputs = torch.nn.Softmax()(model(x_tensor))
5 top classes identified by the model:
(class index) class description: model score
(817) sports car, sport car: 0.530656099319
(479) car wheel: 0.131468653679
(581) grille, radiator grille: 0.109313197434
(511) convertible: 0.103721126914
(436) beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon: 0.0631403699517
perturbated input evaluation:
5 top classes identified by the model:
(class index) class description: model score
(408) amphibian, amphibious vehicle: 0.978337585926
(751) racer, race car, racing car: 0.00674718478695
(814) speedboat: 0.00620162859559
(817) sports car, sport car: 0.00264584389515
(479) car wheel: 0.00228302320465
results saved to:
original image saved to data/perturbations/car/2018-07-22 15:15:42.316242/original.jpg
perturbated image saved to data/perturbations/car/2018-07-22 15:15:42.316242/perturbated.jpg
differences image saved to data/perturbations/car/2018-07-22 15:15:42.316242/differences.jpg
/usr/lib/python2.7/dist-packages/skimage/io/_plugins/pil_plugin.py:96: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
if np.issubdtype(arr.dtype, float):
scores saved to: data/perturbations/car/2018-07-22 15:15:42.316242/scores.csv
perturbation generator model saved to: data/perturbations/car/2018-07-22 15:15:42.316242/pert_model.pt
loss history saved to: data/perturbations/car/2018-07-22 15:15:42.316242/loss_history
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from img_perts import perturbate
file_name = "excavator"
image_path = "data/image_samples/{}.jpg".format(file_name)
save_to_dir = "data/perturbations/{}".format(file_name)
perturbate.main(
image_path=image_path,
kernel_size=3,
nblocks=3,
nlayers=3,
epochs=2000,#000,
lr=0.001,
l1_coeff=1.0,
class_index="max",
init_pert_model=True,
save_to_dir=save_to_dir,
datetime_subdir=True,
)
100%|██████████| 2000/2000 [07:34<00:00, 4.40it/s]
original score: tensor([ 0.9656, 0.0204, 0.0024, 0.0021, 0.0008], device='cuda:0')
perturbed score: tensor(1.00000e-03 *
[ 2.7633, 0.6131, 0.1401, 0.1156, 0.1987], device='cuda:0')
original input evaluation:
5 top classes identified by the model:
(class index) class description: model score
(517) crane: 0.965572297573
(913) wreck: 0.020407076925
(595) harvester, reaper: 0.00241595739499
(510) container ship, containership, container vessel: 0.00212941667996
(730) plow, plough: 0.000826664734632
perturbated input evaluation:
5 top classes identified by the model:
(class index) class description: model score
(674) mousetrap: 0.222181156278
(481) cassette: 0.146628186107
(605) iPod: 0.0957641899586
(710) pencil sharpener: 0.0585163719952
(851) television, television system: 0.056191008538
results saved to:
original image saved to data/perturbations/excavator/2018-07-22 15:46:53.904665/original.jpg
perturbated image saved to data/perturbations/excavator/2018-07-22 15:46:53.904665/perturbated.jpg
differences image saved to data/perturbations/excavator/2018-07-22 15:46:53.904665/differences.jpg
scores saved to: data/perturbations/excavator/2018-07-22 15:46:53.904665/scores.csv
perturbation generator model saved to: data/perturbations/excavator/2018-07-22 15:46:53.904665/pert_model.pt
loss history saved to: data/perturbations/excavator/2018-07-22 15:46:53.904665/loss_history
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from img_perts import perturbate
file_name = "palace"
image_path = "data/image_samples/{}.jpg".format(file_name)
save_to_dir = "data/perturbations/{}".format(file_name)
perturbate.main(
image_path=image_path,
kernel_size=3,
nblocks=3,
nlayers=3,
epochs=2000,#000,
lr=0.001,
l1_coeff=1.0,
class_index="max",
init_pert_model=True,
save_to_dir=save_to_dir,
datetime_subdir=True,
)
100%|██████████| 2000/2000 [07:50<00:00, 4.25it/s]
original score: tensor([ 0.7823, 0.0370, 0.0344, 0.0290, 0.0265], device='cuda:0')
perturbed score: tensor(1.00000e-03 *
[ 2.4122, 0.5660, 1.4265, 0.3527, 1.9176], device='cuda:0')
original input evaluation:
5 top classes identified by the model:
(class index) class description: model score
(483) castle: 0.782301783562
(975) lakeside, lakeshore: 0.037042953074
(576) gondola: 0.0343679413199
(833) submarine, pigboat, sub, U-boat: 0.0290066376328
(536) dock, dockage, docking facility: 0.0265274904668
perturbated input evaluation:
5 top classes identified by the model:
(class index) class description: model score
(526) desk: 0.414985775948
(527) desktop computer: 0.14139971137
(664) monitor: 0.0752502381802
(782) screen, CRT screen: 0.0506206452847
(762) restaurant, eating house, eating place, eatery: 0.0197616908699
results saved to:
original image saved to data/perturbations/palace/2018-07-22 16:02:56.023002/original.jpg
perturbated image saved to data/perturbations/palace/2018-07-22 16:02:56.023002/perturbated.jpg
differences image saved to data/perturbations/palace/2018-07-22 16:02:56.023002/differences.jpg
scores saved to: data/perturbations/palace/2018-07-22 16:02:56.023002/scores.csv
perturbation generator model saved to: data/perturbations/palace/2018-07-22 16:02:56.023002/pert_model.pt
loss history saved to: data/perturbations/palace/2018-07-22 16:02:56.023002/loss_history
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from img_perts import perturbate2
file_name = "dog"
image_path = "data/image_samples/{}.jpg".format(file_name)
save_to_dir = "data/perturbations/{}/to_polar_bear".format(file_name)
perturbate2.main(
image_path=image_path,
kernel_size=3,
nblocks=3,
nlayers=3,
epochs=2000,#000,
lr=0.001,
l1_coeff=1.0,
class_index=296, # (296) ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus
minimize_class=False, # maximize
init_pert_model=True,
save_to_dir=save_to_dir,
datetime_subdir=True,
)
100%|██████████| 2000/2000 [08:13<00:00, 4.05it/s]
img_perts/perturbate.py:229: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
print "original score: {}".format(torch.nn.Softmax()(vgg_model(img))[0, categories])
img_perts/perturbate.py:230: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
print "perturbed score: {}".format(torch.nn.Softmax()(vgg_model(pert_img))[0, categories])
original score: tensor(1.00000e-04 *
[ 5.1595], device='cuda:0')
perturbed score: tensor([ 0.9973], device='cuda:0')
original input evaluation:
img_perts/perturbate.py:377: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
outputs = torch.nn.Softmax()(model(x_tensor))
5 top classes identified by the model:
(class index) class description: model score
(222) kuvasz: 0.823425173759
(257) Great Pyrenees: 0.141241371632
(207) golden retriever: 0.0236044339836
(208) Labrador retriever: 0.00311248097569
(229) Old English sheepdog, bobtail: 0.00158786645625
perturbated input evaluation:
5 top classes identified by the model:
(class index) class description: model score
(296) ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus: 0.997341692448
(222) kuvasz: 0.00108521152288
(257) Great Pyrenees: 0.000606264104135
(270) white wolf, Arctic wolf, Canis lupus tundrarum: 0.00037263968261
(258) Samoyed, Samoyede: 0.000170646904735
results saved to:
original image saved to data/perturbations/dog/to_polar_bear/2018-07-23 15:47:53.201220/original.jpg
perturbated image saved to data/perturbations/dog/to_polar_bear/2018-07-23 15:47:53.201220/perturbated.jpg
differences image saved to data/perturbations/dog/to_polar_bear/2018-07-23 15:47:53.201220/differences.jpg
/usr/lib/python2.7/dist-packages/skimage/io/_plugins/pil_plugin.py:96: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
if np.issubdtype(arr.dtype, float):
scores saved to: data/perturbations/dog/to_polar_bear/2018-07-23 15:47:53.201220/scores.csv
perturbation generator model saved to: data/perturbations/dog/to_polar_bear/2018-07-23 15:47:53.201220/pert_model.pt
loss history saved to: data/perturbations/dog/to_polar_bear/2018-07-23 15:47:53.201220/loss_history
First restart the notebook (the perturbations module for videos is also named "perturbate").
from IPython.core.display import HTML
HTML("<script>Jupyter.notebook.kernel.restart()</script>")%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from video_perts import perturbate
sample_name = "scuba_diving"
video_path = "data/video_samples/{}/processed.mp4".format(sample_name)
save_to_dir = "data/video_perturbations/{}".format(sample_name)
perturbate.main(
input_path=video_path,
kernel_size=(3, 3, 3),
nblocks=3,
nlayers=3,
epochs=2000,
lr=0.01,
l1_coeff=1.0,
init_pert_model=True,
save_to_dir=save_to_dir,
datetime_subdir=True,
graph=False,
loss_figname="loss",
)
100%|██████████| 2000/2000 [1:02:18<00:00, 1.87s/it]
original class score: tensor([ 0.8783, 0.1186, 0.0027, 0.0002, 0.0001], device='cuda:0')
perturbed class score: tensor(1.00000e-04 *
[ 4.0324, 0.0278, 0.0370, 0.0282, 0.1205], device='cuda:0')
original input evaluation:
Top 5 classes and associated probabilities:
(class index) class description: model score
(286) scuba diving: 0.878279447556
(321) snorkeling: 0.118646353483
(62) cleaning pool: 0.00274578807876
(329) springboard diving: 0.000245938339503
(172) jumping into pool: 6.03955049883e-05
perturbated input evaluation:
Top 5 classes and associated probabilities:
(class index) class description: model score
(317) smoking hookah: 0.996110975742
(316) smoking: 0.00334095722064
(286) scuba diving: 0.000403238722356
(341) swimming butterfly stroke: 9.40493191592e-05
(337) surfing water: 1.60787585628e-05
results saved to:
data/video_perturbations/scuba_diving/2018-07-23 03:18:04.169892/differences.mp4
data/video_perturbations/scuba_diving/2018-07-23 03:18:04.169892/original_224x224.mp4
data/video_perturbations/scuba_diving/2018-07-23 03:18:04.169892/perturbated.mp4
perturbation generator model saved to:
data/video_perturbations/scuba_diving/2018-07-23 03:18:04.169892/pert_model.pt
loss history saved to:
data/video_perturbations/scuba_diving/2018-07-23 03:18:04.169892/loss_history
scores saved to: data/video_perturbations/scuba_diving/2018-07-23 03:18:04.169892/scores.csv
save_to_dir = "data/video_perturbations/scuba_diving/2018-07-23 03:18:04.169892"
perturbate.plot_results(save_to_dir)%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from video_perts import perturbate
sample_name = "punching_bag"
video_path = "data/video_samples/{}/processed.mp4".format(sample_name)
save_to_dir = "data/video_perturbations/{}".format(sample_name)
deep_explanation.main(
input_path=video_path,
kernel_size=(3, 3, 3),
nblocks=3,
nlayers=3,
epochs=2000,
lr=0.01,
l1_coeff=1.0,
init_pert_model=True,
save_to_dir=save_to_dir,
datetime_subdir=True,
graph=False,
loss_figname="loss",
)
100%|██████████| 2000/2000 [1:00:25<00:00, 1.81s/it]
original class score: tensor([ 0.9192, 0.0297, 0.0224, 0.0048, 0.0030], device='cuda:0')
perturbed class score: tensor(1.00000e-04 *
[ 2.4181, 5.1474, 1.2727, 0.0508, 0.4780], device='cuda:0')
original input evaluation:
Top 5 classes and associated probabilities:
(class index) class description: model score
(258) punching bag: 0.91917347908
(105) drop kicking: 0.0296538285911
(302) side kick: 0.0223926380277
(259) punching person (boxing): 0.00478684389964
(120) exercising with an exercise ball: 0.00302171171643
perturbated input evaluation:
Top 5 classes and associated probabilities:
(class index) class description: model score
(34) breakdancing: 0.965005874634
(306) skateboarding: 0.014236423187
(173) jumpstyle dancing: 0.00587026821449
(179) krumping: 0.00345934508368
(349) tap dancing: 0.00263738399372
results saved to:
data/video_perturbations/punching_bag/2018-07-23 02:15:40.900206/differences.mp4
data/video_perturbations/punching_bag/2018-07-23 02:15:40.900206/original_224x224.mp4
data/video_perturbations/punching_bag/2018-07-23 02:15:40.900206/perturbated.mp4
perturbation generator model saved to:
data/video_perturbations/punching_bag/2018-07-23 02:15:40.900206/pert_model.pt
loss history saved to:
data/video_perturbations/punching_bag/2018-07-23 02:15:40.900206/loss_history
scores saved to: data/video_perturbations/punching_bag/2018-07-23 02:15:40.900206/scores.csv
save_to_dir = "data/video_perturbations/punching_bag/2018-07-23 02:15:40.900206"
perturbate.plot_results(save_to_dir)%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from video_perts import perturbate
sample_name = "mowing_lawn"
video_path = "data/video_samples/{}/processed.mp4".format(sample_name)
save_to_dir = "data/video_perturbations/{}".format(sample_name)
perturbate.main(
input_path=video_path,
kernel_size=(3, 3, 3),
nblocks=3,
nlayers=3,
epochs=2000,
lr=0.01,
l1_coeff=1.0,
init_pert_model=True,
save_to_dir=save_to_dir,
datetime_subdir=True,
graph=False,
loss_figname="loss",
)
100%|██████████| 2000/2000 [1:02:16<00:00, 1.87s/it]
original class score: tensor([ 0.9727, 0.0244, 0.0028, 0.0000, 0.0000], device='cuda:0')
perturbed class score: tensor(1.00000e-04 *
[ 1.4000, 0.9696, 1.0609, 0.3011, 0.0007], device='cuda:0')
original input evaluation:
Top 5 classes and associated probabilities:
(class index) class description: model score
(201) mowing lawn: 0.972674787045
(378) walking the dog: 0.0244405660778
(363) training dog: 0.00283232703805
(338) sweeping floor: 1.45368485391e-05
(25) blowing leaves: 1.36257431222e-05
perturbated input evaluation:
Top 5 classes and associated probabilities:
(class index) class description: model score
(360) tobogganing: 0.99878436327
(28) bobsledding: 0.00066970550688
(201) mowing lawn: 0.000140000367537
(42) canoeing or kayaking: 0.000116581104521
(363) training dog: 0.000106086059532
results saved to:
data/video_perturbations/mowing_lawn/2018-07-23 01:15:10.280387/differences.mp4
data/video_perturbations/mowing_lawn/2018-07-23 01:15:10.280387/original_224x224.mp4
data/video_perturbations/mowing_lawn/2018-07-23 01:15:10.280387/perturbated.mp4
perturbation generator model saved to:
data/video_perturbations/mowing_lawn/2018-07-23 01:15:10.280387/pert_model.pt
loss history saved to:
data/video_perturbations/mowing_lawn/2018-07-23 01:15:10.280387/loss_history
scores saved to: data/video_perturbations/mowing_lawn/2018-07-23 01:15:10.280387/scores.csv
save_to_dir = "data/video_perturbations/mowing_lawn/2018-07-23 01:15:10.280387"
perturbate.plot_results(save_to_dir)%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from video_perts import perturbate
sample_name = "drinking_beer"
video_path = "data/video_samples/{}/processed.mp4".format(sample_name)
save_to_dir = "data/video_perturbations/{}".format(sample_name)
perturbate.main(
input_path=video_path,
kernel_size=(3, 3, 3),
nblocks=3,
nlayers=3,
epochs=2000,
lr=0.01,
l1_coeff=1.0,
init_pert_model=True,
save_to_dir=save_to_dir,
datetime_subdir=True,
graph=False,
loss_figname="loss",
)
100%|██████████| 2000/2000 [1:02:42<00:00, 1.88s/it]
original class score: tensor([ 0.4608, 0.2248, 0.2237, 0.0446, 0.0321], device='cuda:0')
perturbed class score: tensor(1.00000e-05 *
[ 1.2791, 9.3388, 8.8174, 1.6485, 9.7730], device='cuda:0')
original input evaluation:
Top 5 classes and associated probabilities:
(class index) class description: model score
(352) tasting beer: 0.460781544447
(101) drinking beer: 0.224785089493
(15) bartending: 0.223735421896
(203) opening bottle: 0.0446103103459
(100) drinking: 0.0320644378662
perturbated input evaluation:
Top 5 classes and associated probabilities:
(class index) class description: model score
(313) slacklining: 0.902415752411
(88) deadlifting: 0.0470117554069
(149) headbanging: 0.0131791224703
(255) pull ups: 0.0057046120055
(260) push up: 0.00212896009907
results saved to:
data/video_perturbations/drinking_beer/2018-07-22 22:01:41.900350/differences.mp4
data/video_perturbations/drinking_beer/2018-07-22 22:01:41.900350/original_224x224.mp4
data/video_perturbations/drinking_beer/2018-07-22 22:01:41.900350/perturbated.mp4
perturbation generator model saved to:
data/video_perturbations/drinking_beer/2018-07-22 22:01:41.900350/pert_model.pt
loss history saved to:
data/video_perturbations/drinking_beer/2018-07-22 22:01:41.900350/loss_history
scores saved to: data/video_perturbations/drinking_beer/2018-07-22 22:01:41.900350/scores.csv
save_to_dir = "data/video_perturbations/drinking_beer/2018-07-22 22:01:41.900350"
perturbate.plot_results(save_to_dir)%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from video_perts import perturbate
sample_name = "javelin_throw"
video_path = "data/video_samples/{}/processed.mp4".format(sample_name)
save_to_dir = "data/video_perturbations/{}".format(sample_name)
perturbate.main(
input_path=video_path,
kernel_size=(3, 3, 3),
nblocks=3,
nlayers=3,
epochs=2000,
lr=0.01,
l1_coeff=1.0,
init_pert_model=True,
save_to_dir=save_to_dir,
datetime_subdir=True,
graph=False,
loss_figname="loss",
)
100%|██████████| 2000/2000 [1:05:42<00:00, 1.97s/it]
original class score: tensor([ 0.9913, 0.0042, 0.0012, 0.0010, 0.0008], device='cuda:0')
perturbed class score: tensor(1.00000e-04 *
[ 2.9071, 0.5843, 1.9789, 0.0854, 0.3302], device='cuda:0')
original input evaluation:
Top 5 classes and associated probabilities:
(class index) class description: model score
(166) javelin throw: 0.991295099258
(182) long jump: 0.00420214561746
(50) catching or throwing softball: 0.0011763687944
(358) throwing discus: 0.000968425534666
(367) triple jump: 0.000764829048421
perturbated input evaluation:
Top 5 classes and associated probabilities:
(class index) class description: model score
(164) ice skating: 0.937120079994
(235) playing ice hockey: 0.0256786439568
(280) roller skating: 0.0196170341223
(154) hockey stop: 0.00496697798371
(246) playing tennis: 0.00381563603878
results saved to:
data/video_perturbations/javelin_throw/2018-07-23 00:12:48.856662/differences.mp4
data/video_perturbations/javelin_throw/2018-07-23 00:12:48.856662/original_224x224.mp4
data/video_perturbations/javelin_throw/2018-07-23 00:12:48.856662/perturbated.mp4
perturbation generator model saved to:
data/video_perturbations/javelin_throw/2018-07-23 00:12:48.856662/pert_model.pt
loss history saved to:
data/video_perturbations/javelin_throw/2018-07-23 00:12:48.856662/loss_history
scores saved to: data/video_perturbations/javelin_throw/2018-07-23 00:12:48.856662/scores.csv
save_to_dir = "data/video_perturbations/javelin_throw/2018-07-23 00:12:48.856662"
perturbate.plot_results(save_to_dir)
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