def _get_best_theta(
self,
function_evolution: Callable[[ep.Tensor], ep.Tensor],
best_params: ep.Tensor) -> ep.Tensor:
v_type = function_evolution(best_params)
coefficients = ep.zeros(v_type, 2 * self.T).raw
for i in range(0, self.T):
coefficients[2* i] = 1 - (i / self.T)
coefficients[2 * i + 1] = - coefficients[2* i]
for i, coeff in enumerate(coefficients):
params = coeff * self.theta_max
x_evol = function_evolution(params)
x = ep.where(
atleast_kd(best_params == 0, v_type.ndim),
x_evol,
ep.zeros_like(v_type))
is_advs = self._is_adversarial(x)
best_params = ep.where(
(best_params == 0) * is_advs,
params,
best_params
)
if (best_params != 0).all():
break
return best_params
def _binary_search(self, originals: ep.Tensor, perturbed: ep.Tensor, boost: Optional[bool] = False) -> ep.Tensor:
# Choose upper thresholds in binary search based on constraint.
highs = ep.ones(perturbed, len(perturbed))
d = np.prod(perturbed.shape[1:])
thresholds = self._BS_gamma / (d * math.sqrt(d))
lows = ep.zeros_like(highs)
# Boost Binary search
if boost:
boost_vec = 0.1 * originals + 0.9 * perturbed
is_advs = self._is_adversarial(boost_vec)
is_advs = atleast_kd(is_advs, originals.ndim)
originals = ep.where(is_advs.logical_not(), boost_vec, originals)
perturbed = ep.where(is_advs, boost_vec, perturbed)
# use this variable to check when mids stays constant and the BS has converged
old_mids = highs
iteration = 0
while ep.any(highs - lows > thresholds) and iteration < self._BS_max_iteration:
iteration += 1
mids = (lows + highs) / 2
mids_perturbed = self._project(originals, perturbed, mids)
is_adversarial_ = self._is_adversarial(mids_perturbed)
highs = ep.where(is_adversarial_, mids, highs)
lows = ep.where(is_adversarial_, lows, mids)
# check of there is no more progress due to numerical imprecision
reached_numerical_precision = (old_mids == mids).all()
old_mids = mids
if reached_numerical_precision:
break
results = self._project(originals, perturbed, highs)
return results
def __call__(self, model: Model, inputs: T, criterion: Union[Criterion, T]) -> T:
x, restore_type = ep.astensor_(inputs)
del inputs
criterion = get_criterion(criterion)
is_adversarial = get_is_adversarial(criterion, model)
min_, max_ = model.bounds
target = min_ + self.target * (max_ - min_)
direction = target - x
lower_bound = ep.zeros(x, len(x))
upper_bound = ep.ones(x, len(x))
epsilons = lower_bound
for _ in range(self.binary_search_steps):
eps = atleast_kd(epsilons, x.ndim)
is_adv = is_adversarial(x + eps * direction)
lower_bound = ep.where(is_adv, lower_bound, epsilons)
upper_bound = ep.where(is_adv, epsilons, upper_bound)
epsilons = (lower_bound + upper_bound) / 2
epsilons = upper_bound
eps = atleast_kd(epsilons, x.ndim)
xp = x + eps * direction
return restore_type(xp)
def l2_clipping_aware_rescaling(x,
delta,
eps: float,
a: float = 0.0,
b: float = 1.0): # type: ignore
"""Calculates eta such that norm(clip(x + eta * delta, a, b) - x) == eps.
Assumes x and delta have a batch dimension and eps, a, b, and p are
scalars. If the equation cannot be solved because eps is too large, the
left hand side is maximized.
Args:
x: A batch of inputs (PyTorch Tensor, TensorFlow Eager Tensor, NumPy
Array, JAX Array, or EagerPy Tensor).
delta: A batch of perturbation directions (same shape and type as x).
eps: The target norm (non-negative float).
a: The lower bound of the data domain (float).
b: The upper bound of the data domain (float).
Returns:
eta: A batch of scales with the same number of dimensions as x but all
axis == 1 except for the batch dimension.
"""
(x, delta), restore_fn = ep.astensors_(x, delta)
N = x.shape[0]
assert delta.shape[0] == N
rows = ep.arange(x, N)
delta2 = delta.square().reshape((N, -1))
space = ep.where(delta >= 0, b - x, x - a).reshape((N, -1))
f2 = space.square() / ep.maximum(delta2, 1e-20)
ks = ep.argsort(f2, axis=-1)
f2_sorted = f2[rows[:, ep.newaxis], ks]
m = ep.cumsum(delta2[rows[:, ep.newaxis],
ks.flip(axis=1)], axis=-1).flip(axis=1)
dx = f2_sorted[:, 1:] - f2_sorted[:, :-1]
dx = ep.concatenate((f2_sorted[:, :1], dx), axis=-1)
dy = m * dx
y = ep.cumsum(dy, axis=-1)
c = y >= eps**2
# work-around to get first nonzero element in each row
f = ep.arange(x, c.shape[-1], 0, -1)
j = ep.argmax(c.astype(f.dtype) * f, axis=-1)
eta2 = f2_sorted[rows, j] - (y[rows, j] - eps**2) / m[rows, j]
# it can happen that for certain rows even the largest j is not large enough
# (i.e. c[:, -1] is False), then we will just use it (without any correction) as it's
# the best we can do (this should also be the only cases where m[j] can be
# 0 and they are thus not a problem)
eta2 = ep.where(c[:, -1], eta2, f2_sorted[:, -1])
eta = ep.sqrt(eta2)
eta = eta.reshape((-1, ) + (1, ) * (x.ndim - 1))
# xp = ep.clip(x + eta * delta, a, b)
# l2 = (xp - x).reshape((N, -1)).square().sum(axis=-1).sqrt()
return restore_fn(eta)
def _get_candidates(self, originals: ep.Tensor, best_advs: ep.Tensor) -> ep.Tensor:
"""
Find the lowest epsilon to misclassified x following the direction: q of class 1 / q + eps*direction of class 0
"""
epsilons = ep.zeros(originals, len(originals))
direction_2 = ep.zeros_like(originals)
while (epsilons == 0).any():
# if epsilon ==0, we are still searching a good direction
direction_2 = ep.where(
atleast_kd(epsilons == 0, direction_2.ndim),
self._basis.get_vector(self._directions_ortho),
direction_2
)
for i, eps_i in enumerate(epsilons):
if eps_i == 0:
self._directions_ortho[i] = ep.concatenate((self._directions_ortho[i], direction_2[i].expand_dims(0)), axis=0)
if len(self._directions_ortho[i]) > self.n_ortho + 1:
self._directions_ortho[i] = ep.concatenate((self._directions_ortho[i][:1], self._directions_ortho[i][self.n_ortho:]))
function_evolution = self._get_evolution_function(originals, best_advs, direction_2)
new_epsilons = self._get_best_theta(function_evolution, epsilons)
self.theta_max = ep.where(new_epsilons == 0, self.theta_max * self.rho, self.theta_max)
self.theta_max = ep.where((new_epsilons != 0) * (epsilons == 0), self.theta_max / self.rho, self.theta_max)
epsilons = new_epsilons
function_evolution = self._get_evolution_function(originals, best_advs, direction_2)
if self.with_alpha_line_search:
epsilons = self._binary_search_on_alpha(function_evolution, epsilons)
epsilons = epsilons.expand_dims(0)
if self.with_interpolation:
epsilons = ep.concatenate((epsilons, epsilons[0] / 2), axis=0)
candidates = ep.concatenate([function_evolution(eps).expand_dims(0) for eps in epsilons], axis=0)
if self.with_interpolation:
d = self.distance(best_advs, originals)
delta = self.distance(self._binary_search(originals, candidates[1], boost=True), originals)
theta_star = epsilons[0]
num = theta_star * (4 * delta - d * (self._cos(theta_star.raw) + 3))
den = 4 * (2 * delta - d * (self._cos(theta_star.raw) + 1))
theta_hat = num / den
q_interp = function_evolution(theta_hat)
if self.with_distance_line_search:
q_interp = self._binary_search(originals, q_interp, boost=True)
candidates = ep.concatenate((candidates, q_interp.expand_dims(0)), axis=0)
return candidates
def _project_shrinkage_thresholding(
z: ep.Tensor, x0: ep.Tensor, regularization: float, min_: float, max_: float
) -> ep.Tensor:
"""Performs the element-wise projected shrinkage-thresholding
operation"""
upper_mask = z - x0 > regularization
lower_mask = z - x0 < -regularization
projection = ep.where(upper_mask, ep.minimum(z - regularization, max_), x0)
projection = ep.where(lower_mask, ep.maximum(z + regularization, min_), projection)
return projection
def approximate_gradients(
self,
is_adversarial: Callable[[ep.Tensor], ep.Tensor],
x_advs: ep.Tensor,
steps: int,
delta: ep.Tensor,
) -> ep.Tensor:
# (steps, bs, ...)
noise_shape = tuple([steps] + list(x_advs.shape))
if self.constraint == "l2":
rv = ep.normal(x_advs, noise_shape)
elif self.constraint == "linf":
rv = ep.uniform(x_advs, low=-1, high=1, shape=noise_shape)
rv /= atleast_kd(ep.norms.l2(flatten(rv, keep=1), -1), rv.ndim) + 1e-12
scaled_rv = atleast_kd(ep.expand_dims(delta, 0), rv.ndim) * rv
perturbed = ep.expand_dims(x_advs, 0) + scaled_rv
perturbed = ep.clip(perturbed, 0, 1)
rv = (perturbed - x_advs) / atleast_kd(ep.expand_dims(delta + 1e-8, 0),
rv.ndim)
multipliers_list: List[ep.Tensor] = []
for step in range(steps):
decision = is_adversarial(perturbed[step])
multipliers_list.append(
ep.where(
decision,
ep.ones(
x_advs,
(len(x_advs, )),
),
-ep.ones(
x_advs,
(len(decision, )),
),
))
# (steps, bs, ...)
multipliers = ep.stack(multipliers_list, 0)
vals = ep.where(
ep.abs(ep.mean(multipliers, axis=0, keepdims=True)) == 1,
multipliers,
multipliers - ep.mean(multipliers, axis=0, keepdims=True),
)
grad = ep.mean(atleast_kd(vals, rv.ndim) * rv, axis=0)
grad /= ep.norms.l2(atleast_kd(flatten(grad), grad.ndim)) + 1e-12
return grad
def normalize(self, gradients: ep.Tensor, *, x: ep.Tensor,
bounds: Bounds) -> ep.Tensor:
bad_pos = ep.logical_or(
ep.logical_and(x == bounds.lower, gradients < 0),
ep.logical_and(x == bounds.upper, gradients > 0),
)
gradients = ep.where(bad_pos, ep.zeros_like(gradients), gradients)
abs_gradients = gradients.abs()
quantiles = np.quantile(flatten(abs_gradients).numpy(),
q=self.quantile,
axis=-1)
keep = abs_gradients >= atleast_kd(ep.from_numpy(gradients, quantiles),
gradients.ndim)
e = ep.where(keep, gradients.sign(), ep.zeros_like(gradients))
return normalize_lp_norms(e, p=1)
def run(
self,
model: Model,
inputs: T,
criterion: Union[Criterion, T],
*,
early_stop: Optional[float] = None,
starting_points: Optional[ep.Tensor] = None,
**kwargs: Any,
) -> T:
originals, restore_type = ep.astensor_(inputs)
self._nqueries = {i: 0 for i in range(len(originals))}
self._set_cos_sin_function(originals)
self.theta_max = ep.ones(originals, len(originals)) * self._theta_max
criterion = get_criterion(criterion)
self._criterion_is_adversarial = get_is_adversarial(criterion, model)
# Get Starting Point
if starting_points is not None:
best_advs = starting_points
elif starting_points is None:
init_attack: MinimizationAttack = LinearSearchBlendedUniformNoiseAttack(steps=50)
best_advs = init_attack.run(model, originals, criterion, early_stop=early_stop)
else:
raise ValueError("starting_points {} doesn't exist.".format(starting_points))
assert self._is_adversarial(best_advs).all()
# Initialize the direction orthogonalized with the first direction
fd = best_advs - originals
norm = ep.norms.l2(fd.flatten(1), axis=1)
fd = fd / atleast_kd(norm, fd.ndim)
self._directions_ortho = {i: v.expand_dims(0) for i, v in enumerate(fd)}
# Load Basis
if "basis_params" in kwargs:
self._basis = Basis(originals, **kwargs["basis_params"])
else:
self._basis = Basis(originals)
for _ in range(self._steps):
# Get candidates. Shape: (n_candidates, batch_size, image_size)
candidates = self._get_candidates(originals, best_advs)
candidates = candidates.transpose((1, 0, 2, 3, 4))
best_candidates = ep.zeros_like(best_advs).raw
for i, o in enumerate(originals):
o_repeated = ep.concatenate([o.expand_dims(0)] * len(candidates[i]), axis=0)
index = ep.argmax(self.distance(o_repeated, candidates[i])).raw
best_candidates[i] = candidates[i][index].raw
is_success = self.distance(best_candidates, originals) < self.distance(best_advs, originals)
best_advs = ep.where(atleast_kd(is_success, best_candidates.ndim), ep.astensor(best_candidates), best_advs)
if all(v > self._max_queries for v in self._nqueries.values()):
print("Max queries attained for all the images.")
break
return restore_type(best_advs)
def __call__(self, model: Model, inputs: T, criterion: Union[Criterion, T]) -> T:
x, restore_type = ep.astensor_(inputs)
del inputs
criterion = get_criterion(criterion)
is_adversarial = get_is_adversarial(criterion, model)
min_, max_ = model.bounds
target = min_ + self.target * (max_ - min_)
direction = target - x
best = ep.ones(x, len(x))
epsilon = 0.0
stepsize = 1.0 / self.steps
for _ in range(self.steps):
# TODO: reduce the batch size to the ones that have not yet been sucessful
is_adv = is_adversarial(x + epsilon * direction)
is_best_adv = ep.logical_and(is_adv, best == 1)
best = ep.where(is_best_adv, epsilon, best)
if (best < 1).all():
break
epsilon += stepsize
eps = atleast_kd(best, x.ndim)
xp = x + eps * direction
return restore_type(xp)
def mid_points(
self,
x0: ep.Tensor,
x1: ep.Tensor,
epsilons: ep.Tensor,
bounds: Tuple[float, float],
):
# returns a point between x0 and x1 where
# epsilon = 0 returns x0 and epsilon = 1
delta = x1 - x0
min_, max_ = bounds
s = max_ - min_
# get epsilons in right shape for broadcasting
epsilons = epsilons.reshape(epsilons.shape + (1, ) * (x0.ndim - 1))
clipped_delta = ep.where(delta < -epsilons * s, -epsilons * s, delta)
clipped_delta = ep.where(clipped_delta > epsilons * s, epsilons * s,
clipped_delta)
return x0 + clipped_delta
def __call__(self, inputs, labels, *, steps=1000):
x = ep.astensor(inputs)
y = ep.astensor(labels)
assert x.shape[0] == y.shape[0]
assert y.ndim == 1
assert x.ndim == 4
if self.channel_axis == 1:
h, w = x.shape[2:4]
elif self.channel_axis == 3:
h, w = x.shape[1:3]
else:
raise ValueError(
"expected 'channel_axis' to be 1 or 3, got {channel_axis}")
size = max(h, w)
min_, max_ = self.model.bounds()
x0 = x
x0np = x0.numpy()
epsilons = np.linspace(0, 1, num=steps + 1)[1:]
logits = ep.astensor(self.model.forward(x0.tensor))
classes = logits.argmax(axis=-1)
is_adv = classes != labels
found = is_adv
result = x0
for epsilon in epsilons:
# TODO: reduce the batch size to the ones that haven't been sucessful
sigmas = [epsilon * size] * 4
sigmas[0] = 0
sigmas[self.channel_axis] = 0
# TODO: once we can implement gaussian_filter in eagerpy, avoid converting from numpy
x = gaussian_filter(x0np, sigmas)
x = np.clip(x, min_, max_)
x = ep.from_numpy(x0, x)
logits = ep.astensor(self.model.forward(x.tensor))
classes = logits.argmax(axis=-1)
is_adv = classes != labels
new_adv = ep.logical_and(is_adv, found.logical_not())
result = ep.where(atleast_kd(new_adv, x.ndim), x, result)
found = ep.logical_or(new_adv, found)
if found.all():
break
return result.tensor
def _binary_search(
self,
is_adversarial: Callable[[ep.Tensor], ep.Tensor],
originals: ep.Tensor,
perturbed: ep.Tensor,
) -> ep.Tensor:
# Choose upper thresholds in binary search based on constraint.
d = np.prod(perturbed.shape[1:])
if self.constraint == "linf":
highs = linf(originals, perturbed)
# TODO: Check if the threshold is correct
# empirically this seems to be too low
thresholds = highs * self.gamma / (d * d)
else:
highs = ep.ones(perturbed, len(perturbed))
thresholds = self.gamma / (d * math.sqrt(d))
lows = ep.zeros_like(highs)
# use this variable to check when mids stays constant and the BS has converged
old_mids = highs
while ep.any(highs - lows > thresholds):
mids = (lows + highs) / 2
mids_perturbed = self._project(originals, perturbed, mids)
is_adversarial_ = is_adversarial(mids_perturbed)
highs = ep.where(is_adversarial_, mids, highs)
lows = ep.where(is_adversarial_, lows, mids)
# check of there is no more progress due to numerical imprecision
reached_numerical_precision = (old_mids == mids).all()
old_mids = mids
if reached_numerical_precision:
# TODO: warn user
break
res = self._project(originals, perturbed, highs)
return res
def project(self, x: ep.Tensor, x0: ep.Tensor,
epsilon: ep.Tensor) -> ep.Tensor:
flatten_delta = flatten(x - x0)
abs_delta = abs(flatten_delta)
epsilon = epsilon.astype(int)
rows = range(flatten_delta.shape[0])
idx_sorted = ep.argsort(abs_delta, axis=-1)[rows, -epsilon]
thresholds = (ep.ones_like(flatten_delta).T *
abs_delta[rows, idx_sorted]).T
clipped = ep.where(abs_delta >= thresholds, flatten_delta, 0)
return x0 + clipped.reshape(x0.shape).astype(x0.dtype)
def run(
self,
model: Model,
inputs: T,
criterion: Union[Criterion, T],
**kwargs: Any,
) -> T:
raise_if_kwargs(kwargs)
x, restore_type = ep.astensor_(inputs)
del inputs, kwargs
verify_input_bounds(x, model)
criterion = get_criterion(criterion)
is_adversarial = get_is_adversarial(criterion, model)
found = is_adversarial(x)
results = x
def grid_search_generator() -> Generator[Any, Any, Any]:
dphis = np.linspace(-self.max_rot, self.max_rot, self.num_rots)
dxs = np.linspace(-self.max_trans, self.max_trans, self.num_trans)
dys = np.linspace(-self.max_trans, self.max_trans, self.num_trans)
for dphi in dphis:
for dx in dxs:
for dy in dys:
yield dphi, dx, dy
def random_search_generator() -> Generator[Any, Any, Any]:
dphis = np.random.uniform(-self.max_rot, self.max_rot,
self.random_steps)
dxs = np.random.uniform(-self.max_trans, self.max_trans,
self.random_steps)
dys = np.random.uniform(-self.max_trans, self.max_trans,
self.random_steps)
for dphi, dx, dy in zip(dphis, dxs, dys):
yield dphi, dx, dy
gen = grid_search_generator(
) if self.grid_search else random_search_generator()
for dphi, dx, dy in gen:
# TODO: reduce the batch size to the ones that haven't been successful
x_p = rotate_and_shift(x, translation=(dx, dy), rotation=dphi)
is_adv = is_adversarial(x_p)
new_adv = ep.logical_and(is_adv, found.logical_not())
results = ep.where(atleast_kd(new_adv, x_p.ndim), x_p, results)
found = ep.logical_or(new_adv, found)
if found.all():
break # all images in batch misclassified
return restore_type(results)
def _binary_search(
self,
x_adv_flat: ep.Tensor,
mask: Union[ep.Tensor, List[bool]],
mask_indices: ep.Tensor,
indices: Union[ep.Tensor, List[int]],
adv_values: ep.Tensor,
non_adv_values: ep.Tensor,
original_shape: Tuple,
is_adversarial: Callable,
) -> ep.Tensor:
for i in range(10):
next_values = (adv_values + non_adv_values) / 2
x_adv_flat = ep.index_update(
x_adv_flat, (mask_indices, indices), next_values
)
is_adv = is_adversarial(x_adv_flat.reshape(original_shape))[mask]
adv_values = ep.where(is_adv, next_values, adv_values)
non_adv_values = ep.where(is_adv, non_adv_values, next_values)
return adv_values
def normalize_gradient_l2_norms(grad: ep.Tensor) -> ep.Tensor:
norms = ep.norms.l2(flatten(grad), -1)
# remove zero gradients
grad = ep.where(atleast_kd(norms == 0, grad.ndim),
ep.normal(grad, shape=grad.shape), grad)
# calculate norms again for previously vanishing elements
norms = ep.norms.l2(flatten(grad), -1)
norms = ep.maximum(norms, 1e-12) # avoid division by zero
factor = 1 / norms
factor = atleast_kd(factor, grad.ndim)
return grad * factor
def _apply_decision_rule(
decision_rule: Union[Literal["EN"], Literal["L1"]],
beta: float,
best_advs: ep.Tensor,
best_advs_norms: ep.Tensor,
x_k: ep.Tensor,
x: ep.Tensor,
found_advs: ep.Tensor,
) -> Tuple[ep.Tensor, ep.Tensor]:
if decision_rule == "EN":
norms = beta * flatten(x_k - x).abs().sum(
axis=-1) + flatten(x_k - x).square().sum(axis=-1)
else:
# decision rule = L1
norms = flatten(x_k - x).abs().sum(axis=-1)
new_best = ep.logical_and(norms < best_advs_norms, found_advs)
new_best_kd = atleast_kd(new_best, best_advs.ndim)
best_advs = ep.where(new_best_kd, x_k, best_advs)
best_advs_norms = ep.where(new_best, norms, best_advs_norms)
return best_advs, best_advs_norms
def run(
self,
model: Model,
inputs: T,
criterion: Union[Criterion, T],
*,
early_stop: Optional[float] = None,
**kwargs: Any,
) -> T:
raise_if_kwargs(kwargs)
self.process_raw()
assert self.inputs is not None
assert self.outputs is not None
x, restore_type = ep.astensor_(inputs)
del inputs, kwargs
verify_input_bounds(x, model)
criterion = get_criterion(criterion)
result = x
found = criterion(x, model(x))
batch_size = len(x)
# for every sample try every other sample
index_pools: List[List[int]] = []
for i in range(batch_size):
indices = list(range(batch_size))
indices.remove(i)
indices = list(indices)
np.random.shuffle(indices)
index_pools.append(indices)
for i in range(batch_size - 1):
if found.all():
break
indices = np.array([pool[i] for pool in index_pools])
xp = self.inputs[indices]
yp = self.outputs[indices]
is_adv = criterion(xp, yp)
new_found = ep.logical_and(is_adv, found.logical_not())
result = ep.where(atleast_kd(new_found, result.ndim), xp, result)
found = ep.logical_or(found, new_found)
return restore_type(result)
def _project(self, originals: ep.Tensor, perturbed: ep.Tensor,
epsilons: ep.Tensor) -> ep.Tensor:
"""Clips the perturbations to epsilon and returns the new perturbed
Args:
originals: A batch of reference inputs.
perturbed: A batch of perturbed inputs.
epsilons: A batch of norm values to project to.
Returns:
A tensor like perturbed but with the perturbation clipped to epsilon.
"""
epsilons = atleast_kd(epsilons, originals.ndim)
if self.constraint == "linf":
perturbation = perturbed - originals
# ep.clip does not support tensors as min/max
clipped_perturbed = ep.where(perturbation > epsilons,
originals + epsilons, perturbed)
clipped_perturbed = ep.where(perturbation < -epsilons,
originals - epsilons,
clipped_perturbed)
return clipped_perturbed
else:
return (1.0 - epsilons) * originals + epsilons * perturbed
def run(
self,
model: Model,
inputs: T,
criterion: Union[Criterion, T],
*,
early_stop: Optional[float] = None,
**kwargs: Any,
) -> T:
raise_if_kwargs(kwargs)
x, restore_type = ep.astensor_(inputs)
del inputs, kwargs
verify_input_bounds(x, model)
criterion = get_criterion(criterion)
is_adversarial = get_is_adversarial(criterion, model)
min_, max_ = model.bounds
target = min_ + self.target * (max_ - min_)
direction = target - x
lower_bound = ep.zeros(x, len(x))
upper_bound = ep.ones(x, len(x))
epsilons = lower_bound
for _ in range(self.binary_search_steps):
eps = atleast_kd(epsilons, x.ndim)
is_adv = is_adversarial(x + eps * direction)
lower_bound = ep.where(is_adv, lower_bound, epsilons)
upper_bound = ep.where(is_adv, epsilons, upper_bound)
epsilons = (lower_bound + upper_bound) / 2
epsilons = upper_bound
eps = atleast_kd(epsilons, x.ndim)
xp = x + eps * direction
return restore_type(xp)
def test_pointwise_targeted_attack(
request: Any,
fmodel_and_data_ext_for_attacks: ModeAndDataAndDescription,
attack: fa.PointwiseAttack,
) -> None:
(fmodel, x,
y), real, low_dimensional_input = fmodel_and_data_ext_for_attacks
if not low_dimensional_input or not real:
pytest.skip()
x = (x - fmodel.bounds.lower) / (fmodel.bounds.upper - fmodel.bounds.lower)
fmodel = fmodel.transform_bounds((0, 1))
init_attack = fa.SaltAndPepperNoiseAttack(steps=50)
init_advs = init_attack.run(fmodel, x, y)
logits = fmodel(init_advs)
num_classes = logits.shape[-1]
target_classes = logits.argmax(-1)
target_classes = ep.where(target_classes == y,
(target_classes + 1) % num_classes,
target_classes)
criterion = fbn.TargetedMisclassification(target_classes)
advs = attack.run(fmodel, x, criterion, starting_points=init_advs)
init_norms_l0 = ep.norms.lp(flatten(init_advs - x), p=0, axis=-1)
norms_l0 = ep.norms.lp(flatten(advs - x), p=0, axis=-1)
init_norms_l2 = ep.norms.lp(flatten(init_advs - x), p=2, axis=-1)
norms_l2 = ep.norms.lp(flatten(advs - x), p=2, axis=-1)
is_smaller_l0 = norms_l0 < init_norms_l0
is_smaller_l2 = norms_l2 < init_norms_l2
assert fbn.accuracy(fmodel, advs, y) < fbn.accuracy(fmodel, x, y)
assert fbn.accuracy(fmodel, advs, y) <= fbn.accuracy(fmodel, init_advs, y)
assert fbn.accuracy(fmodel, advs, target_classes) > fbn.accuracy(
fmodel, x, target_classes)
assert fbn.accuracy(fmodel, advs, target_classes) >= fbn.accuracy(
fmodel, init_advs, target_classes)
assert is_smaller_l2.any()
assert is_smaller_l0.any()
def mid_points(
self,
x0: ep.Tensor,
x1: ep.Tensor,
epsilons: ep.Tensor,
bounds: Tuple[float, float],
) -> ep.Tensor:
# returns a point between x0 and x1 where
# epsilon = 0 returns x0 and epsilon = 1
# returns x1
# get epsilons in right shape for broadcasting
epsilons = epsilons.reshape(epsilons.shape + (1, ) * (x0.ndim - 1))
threshold = (bounds[1] - bounds[0]) * (1 - epsilons)
mask = (x1 - x0).abs() > threshold
new_x = ep.where(mask,
x0 + (x1 - x0).sign() * ((x1 - x0).abs() - threshold),
x0)
return new_x
def run(
self,
model: Model,
inputs: T,
criterion: Union[Criterion, T],
*,
early_stop: Optional[float] = None,
**kwargs: Any,
) -> T:
raise_if_kwargs(kwargs)
x, restore_type = ep.astensor_(inputs)
del inputs, kwargs
verify_input_bounds(x, model)
criterion = get_criterion(criterion)
is_adversarial = get_is_adversarial(criterion, model)
min_, max_ = model.bounds
target = min_ + self.target * (max_ - min_)
direction = target - x
best = ep.ones(x, len(x))
epsilon = 0.0
stepsize = 1.0 / self.steps
for _ in range(self.steps):
# TODO: reduce the batch size to the ones that have not yet been sucessful
is_adv = is_adversarial(x + epsilon * direction)
is_best_adv = ep.logical_and(is_adv, best == 1)
best = ep.where(is_best_adv, epsilon, best)
if (best < 1).all():
break # pragma: no cover
epsilon += stepsize
eps = atleast_kd(best, x.ndim)
xp = x + eps * direction
return restore_type(xp)
def __call__(self,
inputs,
labels,
*,
epsilon,
criterion,
repeats=100,
check_trivial=True):
originals = ep.astensor(inputs)
labels = ep.astensor(labels)
def is_adversarial(p: ep.Tensor) -> ep.Tensor:
"""For each input in x, returns true if it is an adversarial for
the given model and criterion"""
logits = self.model.forward(p)
return criterion(originals, labels, p, logits)
x0 = ep.astensor(inputs)
min_, max_ = self.model.bounds()
result = x0
if check_trivial:
found = is_adversarial(result)
else:
found = ep.zeros(x0, len(result)).bool()
for _ in range(repeats):
if found.all():
break
p = self.sample_noise(x0)
norms = self.get_norms(p)
p = p / atleast_kd(norms, p.ndim)
x = x0 + epsilon * p
x = x.clip(min_, max_)
is_adv = is_adversarial(x)
is_new_adv = ep.logical_and(is_adv, ep.logical_not(found))
result = ep.where(atleast_kd(is_new_adv, x.ndim), x, result)
found = ep.logical_or(found, is_adv)
return result.tensor
def run(
self,
model: Model,
inputs: T,
criterion: Union[Criterion, Any] = None,
*,
epsilon: float,
**kwargs: Any,
) -> T:
raise_if_kwargs(kwargs)
x0, restore_type = ep.astensor_(inputs)
criterion_ = get_criterion(criterion)
del inputs, criterion, kwargs
verify_input_bounds(x0, model)
is_adversarial = get_is_adversarial(criterion_, model)
min_, max_ = model.bounds
result = x0
if self.check_trivial:
found = is_adversarial(result)
else:
found = ep.zeros(x0, len(result)).bool()
for _ in range(self.repeats):
if found.all():
break
p = self.sample_noise(x0)
epsilons = self.get_epsilons(x0, p, epsilon, min_=min_, max_=max_)
x = x0 + epsilons * p
x = x.clip(min_, max_)
is_adv = is_adversarial(x)
is_new_adv = ep.logical_and(is_adv, ep.logical_not(found))
result = ep.where(atleast_kd(is_new_adv, x.ndim), x, result)
found = ep.logical_or(found, is_adv)
return restore_type(result)
def __call__(self, model: Model, inputs: T, criterion: Union[Criterion,
T]) -> T:
x, restore_type = ep.astensor_(inputs)
del inputs
criterion = get_criterion(criterion)
is_adversarial = get_is_adversarial(criterion, model)
best = self._attack(model, x, criterion)
best_is_adv = is_adversarial(best)
for _ in range(1, self._times):
xp = self._attack(model, x, criterion)
# assumes xp does not violate the perturbation size constraint
is_adv = is_adversarial(xp)
new_best = ep.logical_and(is_adv, best_is_adv.logical_not())
best = ep.where(atleast_kd(new_best, best.ndim), xp, best)
best_is_adv = ep.logical_or(is_adv, best_is_adv)
return restore_type(best)
def run(
self,
model: Model,
inputs: T,
criterion: Union[Criterion, T],
*,
early_stop: Optional[float] = None,
**kwargs: Any,
) -> T:
raise_if_kwargs(kwargs)
self.process_raw()
assert self.inputs is not None
assert self.outputs is not None
x, restore_type = ep.astensor_(inputs)
del inputs, kwargs
criterion = get_criterion(criterion)
result = x
found = criterion(x, model(x))
dataset_size = len(self.inputs)
batch_size = len(x)
while not found.all():
indices = np.random.randint(0, dataset_size, size=(batch_size, ))
xp = self.inputs[indices]
yp = self.outputs[indices]
is_adv = criterion(xp, yp)
new_found = ep.logical_and(is_adv, found.logical_not())
result = ep.where(atleast_kd(new_found, result.ndim), xp, result)
found = ep.logical_or(found, new_found)
return restore_type(result)
def run(
self,
model: Model,
inputs: T,
criterion: Union[Criterion, T],
*,
early_stop: Optional[float] = None,
**kwargs: Any,
) -> T:
#raise_if_kwargs(kwargs)
x, restore_type = ep.astensor_(inputs)
del inputs, kwargs
verify_input_bounds(x, model)
criterion = get_criterion(criterion)
min_, max_ = model.bounds
logits = model(x)
classes = logits.argsort(axis=-1).flip(axis=-1)
if self.candidates is None:
candidates = logits.shape[-1] # pragma: no cover
else:
candidates = min(self.candidates, logits.shape[-1])
if not candidates >= 2:
raise ValueError( # pragma: no cover
f"expected the model output to have atleast 2 classes, got {logits.shape[-1]}"
)
logging.info(f"Only testing the top-{candidates} classes")
classes = classes[:, :candidates]
N = len(x)
rows = range(N)
loss_fun = self._get_loss_fn(model, classes)
loss_aux_and_grad = ep.value_and_grad_fn(x, loss_fun, has_aux=True)
x0 = x
p_total = ep.zeros_like(x)
for _ in range(self.steps):
# let's first get the logits using k = 1 to see if we are done
diffs = [loss_aux_and_grad(x, 1)]
_, (_, logits), _ = diffs[0]
is_adv = criterion(x, logits)
if is_adv.all():
break
# then run all the other k's as well
# we could avoid repeated forward passes and only repeat
# the backward pass, but this cannot currently be done in eagerpy
diffs += [loss_aux_and_grad(x, k) for k in range(2, candidates)]
# we don't need the logits
diffs_ = [(losses, grad) for _, (losses, _), grad in diffs]
losses = ep.stack([lo for lo, _ in diffs_], axis=1)
grads = ep.stack([g for _, g in diffs_], axis=1)
assert losses.shape == (N, candidates - 1)
assert grads.shape == (N, candidates - 1) + x0.shape[1:]
# calculate the distances
distances = self.get_distances(losses, grads)
assert distances.shape == (N, candidates - 1)
# determine the best directions
best = distances.argmin(axis=1)
distances = distances[rows, best]
losses = losses[rows, best]
grads = grads[rows, best]
assert distances.shape == (N,)
assert losses.shape == (N,)
assert grads.shape == x0.shape
# apply perturbation
distances = distances + 1e-4 # for numerical stability
p_step = self.get_perturbations(distances, grads)
assert p_step.shape == x0.shape
p_total += p_step
# don't do anything for those that are already adversarial
x = ep.where(
atleast_kd(is_adv, x.ndim), x, x0 + (1.0 + self.overshoot) * p_total
)
x = ep.clip(x, min_, max_)
return restore_type(x)
def __call__(
self,
inputs,
labels,
*,
starting_points=None,
init_attack=None,
criterion: Callable = misclassification,
steps=25000,
spherical_step=1e-2,
source_step=1e-2,
source_step_convergance=1e-7,
step_adaptation=1.5,
tensorboard=False,
update_stats_every_k=10,
):
"""Boundary Attack
Differences to the original reference implementation:
* We do not perform internal operations with float64
* The samples within a batch can currently influence each other a bit
* We don't perform the additional convergence confirmation
* The success rate tracking changed a bit
* Some other changes due to batching and merged loops
Parameters
----------
criterion : Callable
A callable that returns true if the given logits of perturbed
inputs should be considered adversarial w.r.t. to the given labels
and unperturbed inputs.
tensorboard : str
The log directory for TensorBoard summaries. If False, TensorBoard
summaries will be disabled (default). If None, the logdir will be
runs/CURRENT_DATETIME_HOSTNAME.
"""
tb = TensorBoard(logdir=tensorboard)
originals = ep.astensor(inputs)
labels = ep.astensor(labels)
def is_adversarial(p: ep.Tensor) -> ep.Tensor:
"""For each input in x, returns true if it is an adversarial for
the given model and criterion"""
logits = self.model.forward(p)
return criterion(originals, labels, p, logits)
if starting_points is None:
if init_attack is None:
init_attack = LinearSearchBlendedUniformNoiseAttack
logging.info(
f"Neither starting_points nor init_attack given. Falling"
f" back to {init_attack.__name__} for initialization.")
starting_points = init_attack(self.model)(inputs, labels)
best_advs = ep.astensor(starting_points)
assert is_adversarial(best_advs).all()
N = len(originals)
ndim = originals.ndim
spherical_steps = ep.ones(originals, N) * spherical_step
source_steps = ep.ones(originals, N) * source_step
tb.scalar("batchsize", N, 0)
# create two queues for each sample to track success rates
# (used to update the hyper parameters)
stats_spherical_adversarial = ArrayQueue(maxlen=100, N=N)
stats_step_adversarial = ArrayQueue(maxlen=30, N=N)
bounds = self.model.bounds()
for step in range(1, steps + 1):
converged = source_steps < source_step_convergance
if converged.all():
break
converged = atleast_kd(converged, ndim)
# TODO: performance: ignore those that have converged
# (we could select the non-converged ones, but we currently
# cannot easily invert this in the end using EagerPy)
unnormalized_source_directions = originals - best_advs
source_norms = l2norms(unnormalized_source_directions)
source_directions = unnormalized_source_directions / atleast_kd(
source_norms, ndim)
# only check spherical candidates every k steps
check_spherical_and_update_stats = step % update_stats_every_k == 0
candidates, spherical_candidates = draw_proposals(
bounds,
originals,
best_advs,
unnormalized_source_directions,
source_directions,
source_norms,
spherical_steps,
source_steps,
)
candidates.dtype == originals.dtype
spherical_candidates.dtype == spherical_candidates.dtype
is_adv = is_adversarial(candidates)
if check_spherical_and_update_stats:
spherical_is_adv = is_adversarial(spherical_candidates)
stats_spherical_adversarial.append(spherical_is_adv)
# TODO: algorithm: the original implementation ignores those samples
# for which spherical is not adversarial and continues with the
# next iteration -> we estimate different probabilities (conditional vs. unconditional)
# TODO: thoughts: should we always track this because we compute it anyway
stats_step_adversarial.append(is_adv)
else:
spherical_is_adv = None
# in theory, we are closer per construction
# but limited numerical precision might break this
distances = l2norms(originals - candidates)
closer = distances < source_norms
is_best_adv = ep.logical_and(is_adv, closer)
is_best_adv = atleast_kd(is_best_adv, ndim)
cond = converged.logical_not().logical_and(is_best_adv)
best_advs = ep.where(cond, candidates, best_advs)
tb.probability("converged", converged, step)
tb.scalar("updated_stats", check_spherical_and_update_stats, step)
tb.histogram("norms", source_norms, step)
tb.probability("is_adv", is_adv, step)
if spherical_is_adv is not None:
tb.probability("spherical_is_adv", spherical_is_adv, step)
tb.histogram("candidates/distances", distances, step)
tb.probability("candidates/closer", closer, step)
tb.probability("candidates/is_best_adv", is_best_adv, step)
tb.probability("new_best_adv_including_converged", is_best_adv,
step)
tb.probability("new_best_adv", cond, step)
if check_spherical_and_update_stats:
full = stats_spherical_adversarial.isfull()
tb.probability("spherical_stats/full", full, step)
if full.any():
probs = stats_spherical_adversarial.mean()
cond1 = ep.logical_and(probs > 0.5, full)
spherical_steps = ep.where(
cond1, spherical_steps * step_adaptation,
spherical_steps)
source_steps = ep.where(cond1,
source_steps * step_adaptation,
source_steps)
cond2 = ep.logical_and(probs < 0.2, full)
spherical_steps = ep.where(
cond2, spherical_steps / step_adaptation,
spherical_steps)
source_steps = ep.where(cond2,
source_steps / step_adaptation,
source_steps)
stats_spherical_adversarial.clear(
ep.logical_or(cond1, cond2))
tb.conditional_mean(
"spherical_stats/isfull/success_rate/mean", probs,
full, step)
tb.probability_ratio("spherical_stats/isfull/too_linear",
cond1, full, step)
tb.probability_ratio(
"spherical_stats/isfull/too_nonlinear", cond2, full,
step)
full = stats_step_adversarial.isfull()
tb.probability("step_stats/full", full, step)
if full.any():
probs = stats_step_adversarial.mean()
# TODO: algorithm: changed the two values because we are currently tracking p(source_step_sucess)
# instead of p(source_step_success | spherical_step_sucess) that was tracked before
cond1 = ep.logical_and(probs > 0.25, full)
source_steps = ep.where(cond1,
source_steps * step_adaptation,
source_steps)
cond2 = ep.logical_and(probs < 0.1, full)
source_steps = ep.where(cond2,
source_steps / step_adaptation,
source_steps)
stats_step_adversarial.clear(ep.logical_or(cond1, cond2))
tb.conditional_mean("step_stats/isfull/success_rate/mean",
probs, full, step)
tb.probability_ratio(
"step_stats/isfull/success_rate_too_high", cond1, full,
step)
tb.probability_ratio(
"step_stats/isfull/success_rate_too_low", cond2, full,
step)
tb.histogram("spherical_step", spherical_steps, step)
tb.histogram("source_step", source_steps, step)
tb.close()
return best_advs.tensor
