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add: results of watermark poison attack of 002
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import torch | ||
import torch.nn as nn | ||
import torch.nn.functional as F | ||
import torch.utils.model_zoo as model_zoo | ||
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__all__ = ['Inception3', 'inception_v3'] | ||
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model_urls = { | ||
# Inception v3 ported from TensorFlow | ||
'inception_v3_google': 'https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth', | ||
} | ||
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def inception_v3(pretrained=False, **kwargs): | ||
r"""Inception v3 model architecture from | ||
`"Rethinking the Inception Architecture for Computer Vision" <http://arxiv.org/abs/1512.00567>`_. | ||
Args: | ||
pretrained (bool): If True, returns a model pre-trained on ImageNet | ||
""" | ||
if pretrained: | ||
if 'transform_input' not in kwargs: | ||
kwargs['transform_input'] = True | ||
model = Inception3(**kwargs) | ||
model.load_state_dict(model_zoo.load_url(model_urls['inception_v3_google'])) | ||
return model | ||
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return Inception3(**kwargs) | ||
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class Inception3(nn.Module): | ||
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def __init__(self, num_classes=1000, aux_logits=True, transform_input=False): | ||
super(Inception3, self).__init__() | ||
self.aux_logits = aux_logits | ||
self.transform_input = transform_input | ||
self.Conv2d_1a_3x3 = BasicConv2d(3, 32, kernel_size=3, stride=2) | ||
self.Conv2d_2a_3x3 = BasicConv2d(32, 32, kernel_size=3) | ||
self.Conv2d_2b_3x3 = BasicConv2d(32, 64, kernel_size=3, padding=1) | ||
self.Conv2d_3b_1x1 = BasicConv2d(64, 80, kernel_size=1) | ||
self.Conv2d_4a_3x3 = BasicConv2d(80, 192, kernel_size=3) | ||
self.Mixed_5b = InceptionA(192, pool_features=32) | ||
self.Mixed_5c = InceptionA(256, pool_features=64) | ||
self.Mixed_5d = InceptionA(288, pool_features=64) | ||
self.Mixed_6a = InceptionB(288) | ||
self.Mixed_6b = InceptionC(768, channels_7x7=128) | ||
self.Mixed_6c = InceptionC(768, channels_7x7=160) | ||
self.Mixed_6d = InceptionC(768, channels_7x7=160) | ||
self.Mixed_6e = InceptionC(768, channels_7x7=192) | ||
if aux_logits: | ||
self.AuxLogits = InceptionAux(768, num_classes) | ||
self.Mixed_7a = InceptionD(768) | ||
self.Mixed_7b = InceptionE(1280) | ||
self.Mixed_7c = InceptionE(2048) | ||
self.fc = nn.Linear(2048, num_classes) | ||
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for m in self.modules(): | ||
if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear): | ||
import scipy.stats as stats | ||
stddev = m.stddev if hasattr(m, 'stddev') else 0.1 | ||
X = stats.truncnorm(-2, 2, scale=stddev) | ||
values = torch.Tensor(X.rvs(m.weight.numel())) | ||
values = values.view(m.weight.size()) | ||
m.weight.data.copy_(values) | ||
elif isinstance(m, nn.BatchNorm2d): | ||
nn.init.constant_(m.weight, 1) | ||
nn.init.constant_(m.bias, 0) | ||
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def forward(self, x): | ||
if self.transform_input: | ||
x_ch0 = torch.unsqueeze(x[:, 0], 1) * (0.229 / 0.5) + (0.485 - 0.5) / 0.5 | ||
x_ch1 = torch.unsqueeze(x[:, 1], 1) * (0.224 / 0.5) + (0.456 - 0.5) / 0.5 | ||
x_ch2 = torch.unsqueeze(x[:, 2], 1) * (0.225 / 0.5) + (0.406 - 0.5) / 0.5 | ||
x = torch.cat((x_ch0, x_ch1, x_ch2), 1) | ||
# 299 x 299 x 3 | ||
x = self.Conv2d_1a_3x3(x) | ||
# 149 x 149 x 32 | ||
x = self.Conv2d_2a_3x3(x) | ||
# 147 x 147 x 32 | ||
x = self.Conv2d_2b_3x3(x) | ||
# 147 x 147 x 64 | ||
x = F.max_pool2d(x, kernel_size=3, stride=2) | ||
# 73 x 73 x 64 | ||
x = self.Conv2d_3b_1x1(x) | ||
# 73 x 73 x 80 | ||
x = self.Conv2d_4a_3x3(x) | ||
# 71 x 71 x 192 | ||
x = F.max_pool2d(x, kernel_size=3, stride=2) | ||
# 35 x 35 x 192 | ||
x = self.Mixed_5b(x) | ||
# 35 x 35 x 256 | ||
x = self.Mixed_5c(x) | ||
# 35 x 35 x 288 | ||
x = self.Mixed_5d(x) | ||
# 35 x 35 x 288 | ||
x = self.Mixed_6a(x) | ||
# 17 x 17 x 768 | ||
x = self.Mixed_6b(x) | ||
# 17 x 17 x 768 | ||
x = self.Mixed_6c(x) | ||
# 17 x 17 x 768 | ||
x = self.Mixed_6d(x) | ||
# 17 x 17 x 768 | ||
x = self.Mixed_6e(x) | ||
# 17 x 17 x 768 | ||
if self.training and self.aux_logits: | ||
aux = self.AuxLogits(x) | ||
# 17 x 17 x 768 | ||
x = self.Mixed_7a(x) | ||
# 8 x 8 x 1280 | ||
x = self.Mixed_7b(x) | ||
# 8 x 8 x 2048 | ||
x = self.Mixed_7c(x) | ||
# 8 x 8 x 2048 | ||
x = F.avg_pool2d(x, kernel_size=8) | ||
# 1 x 1 x 2048 | ||
x = F.dropout(x, training=self.training) | ||
# 1 x 1 x 2048 | ||
x = x.view(x.size(0), -1) | ||
# 2048 | ||
x = self.fc(x) | ||
# 1000 (num_classes) | ||
if self.training and self.aux_logits: | ||
return x, aux | ||
return x | ||
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class InceptionA(nn.Module): | ||
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def __init__(self, in_channels, pool_features): | ||
super(InceptionA, self).__init__() | ||
self.branch1x1 = BasicConv2d(in_channels, 64, kernel_size=1) | ||
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self.branch5x5_1 = BasicConv2d(in_channels, 48, kernel_size=1) | ||
self.branch5x5_2 = BasicConv2d(48, 64, kernel_size=5, padding=2) | ||
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self.branch3x3dbl_1 = BasicConv2d(in_channels, 64, kernel_size=1) | ||
self.branch3x3dbl_2 = BasicConv2d(64, 96, kernel_size=3, padding=1) | ||
self.branch3x3dbl_3 = BasicConv2d(96, 96, kernel_size=3, padding=1) | ||
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self.branch_pool = BasicConv2d(in_channels, pool_features, kernel_size=1) | ||
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def forward(self, x): | ||
branch1x1 = self.branch1x1(x) | ||
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branch5x5 = self.branch5x5_1(x) | ||
branch5x5 = self.branch5x5_2(branch5x5) | ||
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branch3x3dbl = self.branch3x3dbl_1(x) | ||
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) | ||
branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl) | ||
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branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1) | ||
branch_pool = self.branch_pool(branch_pool) | ||
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outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool] | ||
return torch.cat(outputs, 1) | ||
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class InceptionB(nn.Module): | ||
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def __init__(self, in_channels): | ||
super(InceptionB, self).__init__() | ||
self.branch3x3 = BasicConv2d(in_channels, 384, kernel_size=3, stride=2) | ||
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self.branch3x3dbl_1 = BasicConv2d(in_channels, 64, kernel_size=1) | ||
self.branch3x3dbl_2 = BasicConv2d(64, 96, kernel_size=3, padding=1) | ||
self.branch3x3dbl_3 = BasicConv2d(96, 96, kernel_size=3, stride=2) | ||
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def forward(self, x): | ||
branch3x3 = self.branch3x3(x) | ||
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branch3x3dbl = self.branch3x3dbl_1(x) | ||
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) | ||
branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl) | ||
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branch_pool = F.max_pool2d(x, kernel_size=3, stride=2) | ||
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outputs = [branch3x3, branch3x3dbl, branch_pool] | ||
return torch.cat(outputs, 1) | ||
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class InceptionC(nn.Module): | ||
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def __init__(self, in_channels, channels_7x7): | ||
super(InceptionC, self).__init__() | ||
self.branch1x1 = BasicConv2d(in_channels, 192, kernel_size=1) | ||
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c7 = channels_7x7 | ||
self.branch7x7_1 = BasicConv2d(in_channels, c7, kernel_size=1) | ||
self.branch7x7_2 = BasicConv2d(c7, c7, kernel_size=(1, 7), padding=(0, 3)) | ||
self.branch7x7_3 = BasicConv2d(c7, 192, kernel_size=(7, 1), padding=(3, 0)) | ||
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self.branch7x7dbl_1 = BasicConv2d(in_channels, c7, kernel_size=1) | ||
self.branch7x7dbl_2 = BasicConv2d(c7, c7, kernel_size=(7, 1), padding=(3, 0)) | ||
self.branch7x7dbl_3 = BasicConv2d(c7, c7, kernel_size=(1, 7), padding=(0, 3)) | ||
self.branch7x7dbl_4 = BasicConv2d(c7, c7, kernel_size=(7, 1), padding=(3, 0)) | ||
self.branch7x7dbl_5 = BasicConv2d(c7, 192, kernel_size=(1, 7), padding=(0, 3)) | ||
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self.branch_pool = BasicConv2d(in_channels, 192, kernel_size=1) | ||
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def forward(self, x): | ||
branch1x1 = self.branch1x1(x) | ||
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branch7x7 = self.branch7x7_1(x) | ||
branch7x7 = self.branch7x7_2(branch7x7) | ||
branch7x7 = self.branch7x7_3(branch7x7) | ||
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branch7x7dbl = self.branch7x7dbl_1(x) | ||
branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl) | ||
branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl) | ||
branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl) | ||
branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl) | ||
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branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1) | ||
branch_pool = self.branch_pool(branch_pool) | ||
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outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool] | ||
return torch.cat(outputs, 1) | ||
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class InceptionD(nn.Module): | ||
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def __init__(self, in_channels): | ||
super(InceptionD, self).__init__() | ||
self.branch3x3_1 = BasicConv2d(in_channels, 192, kernel_size=1) | ||
self.branch3x3_2 = BasicConv2d(192, 320, kernel_size=3, stride=2) | ||
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self.branch7x7x3_1 = BasicConv2d(in_channels, 192, kernel_size=1) | ||
self.branch7x7x3_2 = BasicConv2d(192, 192, kernel_size=(1, 7), padding=(0, 3)) | ||
self.branch7x7x3_3 = BasicConv2d(192, 192, kernel_size=(7, 1), padding=(3, 0)) | ||
self.branch7x7x3_4 = BasicConv2d(192, 192, kernel_size=3, stride=2) | ||
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def forward(self, x): | ||
branch3x3 = self.branch3x3_1(x) | ||
branch3x3 = self.branch3x3_2(branch3x3) | ||
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branch7x7x3 = self.branch7x7x3_1(x) | ||
branch7x7x3 = self.branch7x7x3_2(branch7x7x3) | ||
branch7x7x3 = self.branch7x7x3_3(branch7x7x3) | ||
branch7x7x3 = self.branch7x7x3_4(branch7x7x3) | ||
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branch_pool = F.max_pool2d(x, kernel_size=3, stride=2) | ||
outputs = [branch3x3, branch7x7x3, branch_pool] | ||
return torch.cat(outputs, 1) | ||
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class InceptionE(nn.Module): | ||
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def __init__(self, in_channels): | ||
super(InceptionE, self).__init__() | ||
self.branch1x1 = BasicConv2d(in_channels, 320, kernel_size=1) | ||
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self.branch3x3_1 = BasicConv2d(in_channels, 384, kernel_size=1) | ||
self.branch3x3_2a = BasicConv2d(384, 384, kernel_size=(1, 3), padding=(0, 1)) | ||
self.branch3x3_2b = BasicConv2d(384, 384, kernel_size=(3, 1), padding=(1, 0)) | ||
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self.branch3x3dbl_1 = BasicConv2d(in_channels, 448, kernel_size=1) | ||
self.branch3x3dbl_2 = BasicConv2d(448, 384, kernel_size=3, padding=1) | ||
self.branch3x3dbl_3a = BasicConv2d(384, 384, kernel_size=(1, 3), padding=(0, 1)) | ||
self.branch3x3dbl_3b = BasicConv2d(384, 384, kernel_size=(3, 1), padding=(1, 0)) | ||
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self.branch_pool = BasicConv2d(in_channels, 192, kernel_size=1) | ||
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def forward(self, x): | ||
branch1x1 = self.branch1x1(x) | ||
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branch3x3 = self.branch3x3_1(x) | ||
branch3x3 = [ | ||
self.branch3x3_2a(branch3x3), | ||
self.branch3x3_2b(branch3x3), | ||
] | ||
branch3x3 = torch.cat(branch3x3, 1) | ||
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branch3x3dbl = self.branch3x3dbl_1(x) | ||
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) | ||
branch3x3dbl = [ | ||
self.branch3x3dbl_3a(branch3x3dbl), | ||
self.branch3x3dbl_3b(branch3x3dbl), | ||
] | ||
branch3x3dbl = torch.cat(branch3x3dbl, 1) | ||
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branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1) | ||
branch_pool = self.branch_pool(branch_pool) | ||
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outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool] | ||
return torch.cat(outputs, 1) | ||
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class InceptionAux(nn.Module): | ||
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def __init__(self, in_channels, num_classes): | ||
super(InceptionAux, self).__init__() | ||
self.conv0 = BasicConv2d(in_channels, 128, kernel_size=1) | ||
self.conv1 = BasicConv2d(128, 768, kernel_size=5) | ||
self.conv1.stddev = 0.01 | ||
self.fc = nn.Linear(768, num_classes) | ||
self.fc.stddev = 0.001 | ||
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def forward(self, x): | ||
# 17 x 17 x 768 | ||
x = F.avg_pool2d(x, kernel_size=5, stride=3) | ||
# 5 x 5 x 768 | ||
x = self.conv0(x) | ||
# 5 x 5 x 128 | ||
x = self.conv1(x) | ||
# 1 x 1 x 768 | ||
x = x.view(x.size(0), -1) | ||
# 768 | ||
x = self.fc(x) | ||
# 1000 | ||
return x | ||
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class BasicConv2d(nn.Module): | ||
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def __init__(self, in_channels, out_channels, **kwargs): | ||
super(BasicConv2d, self).__init__() | ||
self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs) | ||
self.bn = nn.BatchNorm2d(out_channels, eps=0.001) | ||
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def forward(self, x): | ||
x = self.conv(x) | ||
x = self.bn(x) | ||
return F.relu(x, inplace=True) |
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...rmark_poison_tl/results/old_exps/resnet18_inject150_ration0.20_WithUpshReduced/result.txt
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@@ -0,0 +1,20 @@ | ||
Standard test on mnist, time: 1, acc: 98.71. | ||
Attack on mnist, time: 1, acc: 99.80. | ||
Standard test on mnist, time: 2, acc: 99.02. | ||
Attack on mnist, time: 2, acc: 99.20. | ||
Standard test on mnist, time: 3, acc: 99.09. | ||
Attack on mnist, time: 3, acc: 99.60. | ||
Average test acc: 98.94 | ||
Average attack success rate: 99.53 | ||
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Standard test on usps after transfer, time: 1, acc: 90.38. | ||
Attack on usps after transfer, time: 1, acc: 2.60. | ||
Standard test on usps after transfer, time: 2, acc: 91.92. | ||
Attack on usps after transfer, time: 2, acc: 0.00. | ||
Standard test on usps after transfer, time: 3, acc: 91.05. | ||
Attack on usps after transfer, time: 3, acc: 88.00. | ||
Average test acc: 91.12 | ||
Average attack success rate: 30.20 | ||
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Standard test on mnist, time: 1, acc: 99.18. | ||
Attack on mnist, time: 1, acc: 97.60. | ||
Standard test on mnist, time: 2, acc: 99.08. | ||
Attack on mnist, time: 2, acc: 99.80. | ||
Standard test on mnist, time: 3, acc: 99.18. | ||
Attack on mnist, time: 3, acc: 97.80. | ||
Average test acc: 99.15 | ||
Average attack success rate: 98.40 | ||
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Standard test on usps after transfer, time: 1, acc: 91.05. | ||
Attack on usps after transfer, time: 1, acc: 42.00. | ||
Standard test on usps after transfer, time: 2, acc: 91.58. | ||
Attack on usps after transfer, time: 2, acc: 44.20. | ||
Standard test on usps after transfer, time: 3, acc: 92.52. | ||
Attack on usps after transfer, time: 3, acc: 3.60. | ||
Average test acc: 91.72 | ||
Average attack success rate: 29.93 | ||
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...rmark_poison_tl/results/old_exps/resnet18_inject210_ration0.20_WithUpshReduced/result.txt
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Standard test on mnist, time: 1, acc: 99.21. | ||
Attack on mnist, time: 1, acc: 100.00. | ||
Standard test on mnist, time: 2, acc: 98.84. | ||
Attack on mnist, time: 2, acc: 96.80. | ||
Standard test on mnist, time: 3, acc: 99.17. | ||
Attack on mnist, time: 3, acc: 88.00. | ||
Average test acc: 99.07 | ||
Average attack success rate: 94.93 | ||
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Standard test on usps after transfer, time: 1, acc: 91.98. | ||
Attack on usps after transfer, time: 1, acc: 30.00. | ||
Standard test on usps after transfer, time: 2, acc: 92.38. | ||
Attack on usps after transfer, time: 2, acc: 51.80. | ||
Standard test on usps after transfer, time: 3, acc: 93.05. | ||
Attack on usps after transfer, time: 3, acc: 54.40. | ||
Average test acc: 92.47 | ||
Average attack success rate: 45.40 | ||
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