def build(self):
network1 = SimpleNet1(in_ch=1, out_ch=10)
network2 = SimpleNet2(in_ch=1, out_ch=10)
detector1 = SimpleDetector(network1)
module1 = DAGModule([network1, network2], dependency=[[-1], [0]], output=[1], device=self.device)
module2 = DAGModule([network1, detector1], dependency=[[-1], [0]], output=[1], device=self.device)
instance = DefenseInstance(model=module1, detector=module2)
return instance
def build(self):
ITdefense = InputTransformation(degrees=(-30, 30))
TEdefense = ThermometerEncoding((0, 1), num_space=10)
Jpegdefense = JpegCompression((0, 1), 30)
RSdefense = ReverseSigmoid()
network1 = SimpleNet(in_ch=1, out_ch=10)
network2 = SimpleNet(in_ch=10, out_ch=10)
network3 = SimpleNet(in_ch=1, out_ch=10)
module1 = DAGModule([TEdefense, network2], device=self.device)
module2 = DAGModule([Jpegdefense, network1, RSdefense], device=self.device)
module3 = DAGModule([ITdefense, network3], device=self.device)
losscomb = LossCombine()
classifier = DAGModule([module1, module2, module3, losscomb], dependency=[[-1], [-1], [-1], [0, 1, 2]],
output=[3], device=self.device)
instance = DefenseInstance(model=classifier)
return instance
def build(self):
Jpegdefense = JpegCompression((0, 1), COMPRESSION_QUALITY)
RSdefense = ReverseSigmoid()
classifier = DAGModule([Jpegdefense, self.network, RSdefense],
device=self.device)
instance = DefenseInstance(model=classifier, detector=None)
return instance
def build(self):
Jpegdefense = JpegCompression((0, 1), 30)
RSdefense = ReverseSigmoid()
network1 = SimpleNet(in_ch=1, out_ch=10)
classifier = DAGModule([Jpegdefense, network1, RSdefense], device=self.device)
instance = DefenseInstance(model=classifier, detector=None)
return instance
def build(self):
ITdefense = InputTransformation(degrees=(-30, 30))
# Varmin = VarianceMinimization(prob=0.3, norm=2)
# Jpegdefense = JpegCompression((0, 1), 30)
# RSdefense = ReverseSigmoid()
network1 = SimpleNet(in_ch=1, out_ch=10)
classifier = DAGModule([ITdefense, network1], device=self.device)
instance = DefenseInstance(model=classifier, detector=None)
return instance
def build(self):
from zoo.saved_instances.JEM.CCF_model import CCF
# nsteps = 10
ITdefense = InputTransformation(degrees=(-ROTATION_DEGREES, ROTATION_DEGREES))
classifier = DAGModule([ITdefense, self.network], device=self.device)
# classifier = WrapperModel(f, nsteps).to(self.device)
# classifier = gradient_attack_wrapper(f)
# classifier = f.to(self.device)
# classifier = nn.DataParallel(classifier).to(self.device)
instance = DefenseInstance(model=classifier, detector=None)
return instance
def build(self):
from zoo.saved_instances.JEM.CCF_model import CCF
Jpegdefense = JpegCompression((0, 1), COMPRESSION_QUALITY)
RSdefense = ReverseSigmoid()
classifier = DAGModule([Jpegdefense, self.network, RSdefense], device=self.device)
# classifier = WrapperModel(f, nsteps).to(self.device)
# classifier = gradient_attack_wrapper(f)
# classifier = f.to(self.device)
# classifier = nn.DataParallel(classifier).to(self.device)
instance = DefenseInstance(model=classifier, detector=None)
return instance
def build(self):
network1 = ConvMedBig(device=self.device,
dataset='cifar10',
width1=4,
width2=4,
width3=4,
linear_size=200,
input_channel=3,
with_normalization=True)
classifier = DAGModule([network1], device=self.device)
instance = DefenseInstance(model=classifier, detector=None)
return instance
def build(self):
ITdefense = InputTransformation(degrees=(-30, 30))
TEdefense = ThermometerEncoding((0, 1), num_space=10)
Jpegdefense = JpegCompression((0, 1), 30)
RSdefense = ReverseSigmoid()
network1 = ConvMedBig(device=self.device,
dataset='cifar10',
width1=4,
width2=4,
width3=4,
linear_size=200,
input_channel=3,
with_normalization=True)
network2 = ConvMedBig(device=self.device,
dataset='cifar10',
width1=4,
width2=4,
width3=2,
linear_size=200,
input_channel=30,
with_normalization=False)
network3 = ConvMedBig(device=self.device,
dataset='cifar10',
width1=4,
width2=4,
width3=4,
linear_size=200,
input_channel=3,
with_normalization=True)
module1 = DAGModule([TEdefense, network2], device=self.device)
module2 = DAGModule([Jpegdefense, network1, RSdefense],
device=self.device)
module3 = DAGModule([ITdefense, network3], device=self.device)
losscomb = LossCombine()
classifier = DAGModule([module1, module2, module3, losscomb],
dependency=[[-1], [-1], [-1], [0, 1, 2]],
output=[3],
device=self.device)
instance = DefenseInstance(model=classifier)
return instance
def build(self):
TEdefense = ThermometerEncoding((0, 1), num_space=10)
network1 = ConvMedBig(device=self.device,
dataset='cifar10',
width1=4,
width2=4,
width3=2,
linear_size=200,
input_channel=30,
with_normalization=False)
classifier = DAGModule([TEdefense, network1], device=self.device)
instance = DefenseInstance(model=classifier, detector=None)
return instance
def build(self):
network = ConvMedBig(device=self.device,
dataset='cifar10',
width1=4,
width2=4,
width3=4,
linear_size=200,
input_channel=3,
with_normalization=True)
network1 = ConvMedBig1(network)
network2 = ConvMedBig2(network)
detector1 = SimpleDetector(network1, 200)
module1 = DAGModule([network1, network2],
dependency=[[-1], [0]],
output=[1],
device=self.device)
module2 = DAGModule([network1, detector1],
dependency=[[-1], [0]],
output=[1],
device=self.device)
classifier = module1
instance = DefenseInstance(model=classifier, detector=module2)
# return instance
return instance
def build(self):
Jpegdefense = JpegCompression((0, 1), 60)
RSdefense = ReverseSigmoid()
network1 = ConvMedBig(device=self.device,
dataset='cifar10',
width1=4,
width2=4,
width3=4,
linear_size=200,
input_channel=3,
with_normalization=True)
classifier = DAGModule([Jpegdefense, network1, RSdefense],
device=self.device)
instance = DefenseInstance(model=classifier, detector=None)
return instance
def build(self):
TEdefense = ThermometerEncoding((0, 1), num_space=10)
network1 = SimpleNet(in_ch=10, out_ch=10)
classifier = DAGModule([TEdefense, network1], device=self.device)
instance = DefenseInstance(model=classifier, detector=None)
return instance
def build(self):
network1 = SimpleNet(in_ch=1, out_ch=10)
classifier = DAGModule([network1], device=self.device)
instance = DefenseInstance(model=classifier, detector=None)
return instance
def build(self):
network = resnet.SparseResNet18(sparsities=[0.1, 0.1, 0.1, 0.1],
sparse_func='reg').to(self.device)
classifier = DAGModule([network], device=self.device)
instance = DefenseInstance(model=classifier, detector=None)
return instance
def build(self):
ITdefense = InputTransformation(degrees=(-ROTATION_DEGREES, ROTATION_DEGREES))
classifier = DAGModule([ITdefense, self.network], device=self.device)
instance = DefenseInstance(model=classifier, detector=None)
return instance