def perturb_iterative(xvar,
yvar,
predict,
nb_iter,
eps,
eps_iter,
loss_fn,
delta_init=None,
minimize=False,
ord=np.inf,
clip_min=0.0,
clip_max=1.0):
"""
Iteratively maximize the loss over the input. It is a shared method for
iterative attacks including IterativeGradientSign, LinfPGD, etc.
:param xvar: input data.
:param yvar: input labels.
:param predict: forward pass function.
:param nb_iter: number of iterations.
:param eps: maximum distortion.
:param eps_iter: attack step size.
:param loss_fn: loss function.
:param delta_init: (optional) tensor contains the random initialization.
:param minimize: (optional bool) whether to minimize or maximize the loss.
:param ord: (optional) the order of maximum distortion (inf or 2).
:param clip_min: mininum value per input dimension.
:param clip_max: maximum value per input dimension.
:return: tensor containing the perturbed input.
"""
if delta_init is not None:
delta = delta_init
else:
delta = torch.zeros_like(xvar)
delta.requires_grad_()
for ii in range(nb_iter):
outputs = predict(xvar + delta)
loss = loss_fn(outputs, yvar)
if minimize:
loss = -loss
loss.backward()
if ord == np.inf:
grad_sign = delta.grad.data.sign()
delta.data = delta.data + batch_multiply(eps_iter, grad_sign)
delta.data = batch_clamp(eps, delta.data)
delta.data = clamp(xvar.data + delta.data, clip_min,
clip_max) - xvar.data
elif ord == 2:
grad = delta.grad.data
grad = normalize_by_pnorm(grad)
delta.data = delta.data + batch_multiply(eps_iter, grad)
delta.data = clamp(xvar.data + delta.data, clip_min,
clip_max) - xvar.data
if eps is not None:
delta.data = clamp_by_pnorm(delta.data, ord, eps)
else:
error = "Only ord = inf and ord = 2 have been implemented"
raise NotImplementedError(error)
delta.grad.data.zero_()
x_adv = clamp(xvar + delta, clip_min, clip_max)
return x_adv
def perturb_iterative(xvar, yvar, predict, nb_iter, eps, eps_iter, loss_fn,
delta_init=None, minimize=False, ord=np.inf,
clip_min=0.0, clip_max=1.0,
l1_sparsity=None, accumulate_param_grad_prob=0., return_itermediate=False):
"""
Iteratively maximize the loss over the input. It is a shared method for
iterative attacks including IterativeGradientSign, LinfPGD, etc.
:param xvar: input data.
:param yvar: input labels.
:param predict: forward pass function.
:param nb_iter: number of iterations.
:param eps: maximum distortion.
:param eps_iter: attack step size.
:param loss_fn: loss function.
:param delta_init: (optional) tensor contains the random initialization.
:param minimize: (optional bool) whether to minimize or maximize the loss.
:param ord: (optional) the order of maximum distortion (inf or 2).
:param clip_min: mininum value per input dimension.
:param clip_max: maximum value per input dimension.
:param l1_sparsity: sparsity value for L1 projection.
- if None, then perform regular L1 projection.
- if float value, then perform sparse L1 descent from
Algorithm 1 in https://arxiv.org/pdf/1904.13000v1.pdf
:return: tensor containing the perturbed input.
"""
if delta_init is not None:
delta = delta_init
else:
delta = torch.zeros_like(xvar)
deltas = []
delta.requires_grad_()
for ii in range(nb_iter):
outputs = predict(xvar + delta)
loss = loss_fn(outputs, yvar)
if minimize:
loss = -loss
store_grad = bool(np.random.binomial(1, accumulate_param_grad_prob))
if store_grad:
loss.backward()
else:
delta.grad = torch.autograd.grad(loss, [delta])[0]
if ord == np.inf:
grad_sign = delta.grad.data.sign()
delta.data = delta.data + batch_multiply(eps_iter, grad_sign)
delta.data = batch_clamp(eps, delta.data)
delta.data = clamp(xvar.data + delta.data, clip_min, clip_max
) - xvar.data
elif ord == 2:
grad = delta.grad.data
grad = normalize_by_pnorm(grad)
delta.data = delta.data + batch_multiply(eps_iter, grad)
delta.data = clamp(xvar.data + delta.data, clip_min, clip_max
) - xvar.data
if eps is not None:
delta.data = clamp_by_pnorm(delta.data, ord, eps)
elif ord == 1:
grad = delta.grad.data
abs_grad = torch.abs(grad)
batch_size = grad.size(0)
view = abs_grad.view(batch_size, -1)
view_size = view.size(1)
if l1_sparsity is None:
vals, idx = view.topk(1)
else:
vals, idx = view.topk(
int(np.round((1 - l1_sparsity) * view_size)))
out = torch.zeros_like(view).scatter_(1, idx, vals)
out = out.view_as(grad)
grad = grad.sign() * (out > 0).float()
grad = normalize_by_pnorm(grad, p=1)
delta.data = delta.data + batch_multiply(eps_iter, grad)
delta.data = batch_l1_proj(delta.data.cpu(), eps)
if xvar.is_cuda:
delta.data = delta.data.cuda()
delta.data = clamp(xvar.data + delta.data, clip_min, clip_max
) - xvar.data
else:
error = "Only ord = inf, ord = 1 and ord = 2 have been implemented"
raise NotImplementedError(error)
delta.grad.data.zero_()
deltas.append(delta.detach())
if return_itermediate:
x_adv = torch.stack([clamp(xvar + d, clip_min, clip_max) for d in deltas], dim=1)
else:
x_adv = clamp(xvar + delta, clip_min, clip_max)
return x_adv
def perturb_iterative_fool_many(xvar,
embvar,
indlistvar,
yvar,
predict,
nb_iter,
eps,
epscand,
eps_iter,
loss_fn,
rayon,
delta_init=None,
minimize=False,
ord=np.inf,
clip_min=0.0,
clip_max=1.0,
l1_sparsity=None):
"""
Iteratively maximize the loss over the input. It is a shared method for
iterative attacks including IterativeGradientSign, LinfPGD, etc.
:param xvar: input data.
:param yvar: input labels.
:param predict: forward pass function.
:param nb_iter: number of iterations.
:param eps: maximum distortion.
:param eps_iter: attack step size.
:param loss_fn: loss function.
:param delta_init: (optional) tensor contains the random initialization.
:param minimize: (optional bool) whether to minimize or maximize the loss.
:param ord: (optional) the order of maximum distortion (inf or 2).
:param clip_min: mininum value per input dimension.
:param clip_max: maximum value per input dimension.
:param l1_sparsity: sparsity value for L1 projection.
- if None, then perform regular L1 projection.
- if float value, then perform sparse L1 descent from
Algorithm 1 in https://arxiv.org/pdf/1904.13000v1.pdf
:return: tensor containing the perturbed input.
"""
#will contain all words encountered during PGD
nb = len(indlistvar)
tablist = []
for t in range(nb):
tablist += [[]]
fool = False
#contain each loss on embed and each difference of loss on word nearest neighboor
loss_memory = np.zeros((nb_iter, ))
word_balance_memory = np.zeros((nb_iter, ))
candid = [torch.empty(0)] * nb
convers = [[]] * nb
for u in range(nb):
#prepare all potential candidates, once and for all
candidates = torch.empty([0, 768]).to(device)
conversion = []
emb_matrix = model.roberta.embeddings.word_embeddings.weight
normed_emb_matrix = F.normalize(emb_matrix, p=2, dim=1)
normed_emb_word = F.normalize(embvar[0][indlistvar[u]], p=2, dim=0)
cosine_similarity = torch.matmul(
normed_emb_word, torch.transpose(normed_emb_matrix, 0, 1))
for t in range(
len(cosine_similarity)): #evitez de faire DEUX boucles .
if cosine_similarity[t] > epscand:
if levenshtein(
tokenizer.decode(
torch.tensor([xvar[0][indlistvar[u]]])),
tokenizer.decode(torch.tensor([t]))) != 1:
candidates = torch.cat(
(candidates, normed_emb_matrix[t].unsqueeze(0)), 0)
conversion += [t]
candid[u] = candidates
convers[u] = conversion
print("nb of candidates :")
print(len(conversion))
#U, S, V = torch.svd(model.roberta.embeddings.word_embeddings.weight)
if delta_init is not None:
delta = delta_init
else:
delta = torch.zeros_like(embvar)
#PGD
delta.requires_grad_()
ii = 0
while ii < nb_iter and not (fool):
outputs = predict(xvar, embvar + delta)
loss = loss_fn(outputs, yvar)
if minimize:
loss = -loss
loss.backward()
if ord == np.inf:
grad_sign = delta.grad.data.sign()
grad_sign = tozerolist(grad_sign, indlistvar)
delta.data = delta.data + batch_multiply(eps_iter, grad_sign)
delta.data = batch_clamp(eps, delta.data)
delta.data = clamp(
embvar.data + delta.data,
clip_min,
clip_max #à retirer?
) - embvar.data
with torch.no_grad():
delta.data = tozero(delta.data, indlistvar)
if (ii % 300) == 0:
adverslist = []
for t in range(nb):
advers, nb_vois = neighboors_np_dens_cand(
(embvar + delta)[0][indlistvar[t]], rayon,
candid[t])
advers = int(advers[0])
advers = torch.tensor(convers[t][advers])
if len(tablist[t]) == 0:
tablist[t] += [
(tokenizer.decode(advers.unsqueeze(0)), ii,
nb_vois)
]
elif not (first(
tablist[t][-1]) == tokenizer.decode(
advers.unsqueeze(0))):
tablist[t] += [
(tokenizer.decode(advers.unsqueeze(0)), ii,
nb_vois)
]
adverslist += [advers]
word_balance_memory[ii] = float(
model(replacelist(xvar, indlistvar, adverslist),
labels=1 - yvar)[0]) - float(
model(replacelist(
xvar, indlistvar, adverslist),
labels=yvar)[0])
if word_balance_memory[ii] < 0:
fool = True
elif ord == 0:
grad = delta.grad.data
grad = tozero(grad, indlistvar)
grad = torch.matmul(
torch.cat((torch.matmul(grad, v)[:, :, :50],
torch.zeros([768 - 50]).to(device)), 2), v.t())
delta.data = delta.data + batch_multiply(eps_iter, grad)
delta.data[0] = my_proj_all(embvar.data[0] + delta.data[0],
embvar[0], indlistvar,
eps) - embvar.data[0]
delta.data = clamp(embvar.data + delta.data, clip_min,
clip_max) - embvar.data #à virer je pense
with torch.no_grad():
delta.data = tozero(delta.data, indlistvar)
if (ii % 300) == 0:
adverslist = []
for t in range(nb):
advers, nb_vois = neighboors_np_dens_cand(
(embvar + delta)[0][indlistvar[t]], rayon,
candid[t])
advers = int(advers[0])
advers = torch.tensor(convers[t][advers])
if len(tablist[t]) == 0:
tablist[t] += [
(tokenizer.decode(advers.unsqueeze(0)), ii,
nb_vois)
]
elif not (first(
tablist[t][-1]) == tokenizer.decode(
advers.unsqueeze(0))):
tablist[t] += [
(tokenizer.decode(advers.unsqueeze(0)), ii,
nb_vois)
]
adverslist += [advers]
word_balance_memory[ii] = float(
model(replacelist(xvar, indlistvar, adverslist),
labels=1 - yvar)[0]) - float(
model(replacelist(
xvar, indlistvar, adverslist),
labels=yvar)[0])
if word_balance_memory[ii] < 0:
fool = True
elif ord == 2:
grad = delta.grad.data
grad = tozero(grad, indlistvar)
grad = normalize_by_pnorm(grad)
delta.data = delta.data + batch_multiply(eps_iter, grad)
delta.data = clamp(embvar.data + delta.data, clip_min,
clip_max) - embvar.data
if eps is not None:
delta.data = clamp_by_pnorm(delta.data, ord, eps)
with torch.no_grad():
delta.data = tozero(delta.data, indlistvar)
if (ii % 300) == 0:
adverslist = []
for t in range(nb):
advers, nb_vois = neighboors_np_dens_cand(
(embvar + delta)[0][indlistvar[t]], rayon,
candid[t])
advers = int(advers[0])
advers = torch.tensor(convers[t][advers])
if len(tablist[t]) == 0:
tablist[t] += [
(tokenizer.decode(advers.unsqueeze(0)), ii,
nb_vois)
]
elif not (first(
tablist[t][-1]) == tokenizer.decode(
advers.unsqueeze(0))):
tablist[t] += [
(tokenizer.decode(advers.unsqueeze(0)), ii,
nb_vois)
]
adverslist += [advers]
word_balance_memory[ii] = float(
model(replacelist(xvar, indlistvar, adverslist),
labels=1 - yvar)[0]) - float(
model(replacelist(
xvar, indlistvar, adverslist),
labels=yvar)[0])
if word_balance_memory[ii] < 0:
fool = True
elif ord == 1:
grad = delta.grad.data
grad_sign = tozero(grad_sign, indvar)
abs_grad = torch.abs(grad)
batch_size = grad.size(0)
view = abs_grad.view(batch_size, -1)
view_size = view.size(1)
if l1_sparsity is None:
vals, idx = view.topk(1)
else:
vals, idx = view.topk(
int(np.round((1 - l1_sparsity) * view_size)))
out = torch.zeros_like(view).scatter_(1, idx, vals)
out = out.view_as(grad)
grad = grad.sign() * (out > 0).float()
grad = normalize_by_pnorm(grad, p=1)
delta.data = delta.data + batch_multiply(eps_iter, grad)
delta.data = batch_l1_proj(delta.data.cpu(), eps)
if embvar.is_cuda:
delta.data = delta.data.cuda()
delta.data = clamp(embvar.data + delta.data, clip_min,
clip_max) - embvar.data
else:
error = "Only ord = inf, ord = 1 and ord = 2 have been implemented"
raise NotImplementedError(error)
delta.grad.data.zero_()
with torch.no_grad():
loss_memory[ii] = loss
ii += 1
#plt.plot(loss_memory)
#plt.title("evolution of embed loss")
#plt.show()
#plt.plot(word_balance_memory)
#plt.title("evolution of word loss difference")
#plt.show()
emb_adv = clamp(embvar + delta, clip_min, clip_max)
return emb_adv, word_balance_memory, loss_memory, tablist, fool
def perturb_russian_roulette(xvar,
yvar,
predict,
stop_prob,
eps,
eps_iter,
loss_fn,
delta_init=None,
minimize=False,
ord=np.inf,
clip_min=0.0,
clip_max=1.0,
l1_sparsity=None):
"""
Iteratively maximize the loss over the input. It is a shared method for
iterative attacks including IterativeGradientSign, LinfPGD, etc.
:param xvar: input data.
:param yvar: input labels.
:param predict: forward pass function.
:param stop_prob: probability of stopping at each iteration.
:param eps: maximum distortion.
:param eps_iter: attack step size.
:param loss_fn: loss function.
:param delta_init: (optional) tensor contains the random initialization.
:param minimize: (optional bool) whether to minimize or maximize the loss.
:param ord: (optional) the order of maximum distortion (inf or 2).
:param clip_min: mininum value per input dimension.
:param clip_max: maximum value per input dimension.
:param l1_sparsity: sparsity value for L1 projection.
- if None, then perform regular L1 projection.
- if float value, then perform sparse L1 descent from
Algorithm 1 in https://arxiv.org/pdf/1904.13000v1.pdf
:return: tensor containing the perturbed input.
"""
if delta_init is not None:
delta = delta_init
else:
delta = torch.zeros_like(xvar)
delta.requires_grad_()
continue_prob = 1.
delta_rr = delta.clone().detach()
coin = bernoulli(1 - stop_prob)
while coin.rvs():
outputs = predict(xvar + delta)
loss = loss_fn(outputs, yvar)
if minimize:
loss = -loss
loss.backward()
delta_prev = delta.clone().detach()
if ord == np.inf:
grad_sign = delta.grad.data.sign()
delta.data = delta.data + batch_multiply(eps_iter, grad_sign)
delta.data = batch_clamp(eps, delta.data)
delta.data = clamp(xvar.data + delta.data, clip_min,
clip_max) - xvar.data
elif ord == 2:
grad = delta.grad.data
grad = normalize_by_pnorm(grad)
delta.data = delta.data + batch_multiply(eps_iter, grad)
delta.data = clamp(xvar.data + delta.data, clip_min,
clip_max) - xvar.data
if eps is not None:
delta.data = clamp_by_pnorm(delta.data, ord, eps)
elif ord == 1:
grad = delta.grad.data
abs_grad = torch.abs(grad)
batch_size = grad.size(0)
view = abs_grad.view(batch_size, -1)
view_size = view.size(1)
if l1_sparsity is None:
vals, idx = view.topk(1)
else:
vals, idx = view.topk(
int(np.round((1 - l1_sparsity) * view_size)))
out = torch.zeros_like(view).scatter_(1, idx, vals)
out = out.view_as(grad)
grad = grad.sign() * (out > 0).float()
grad = normalize_by_pnorm(grad, p=1)
delta.data = delta.data + batch_multiply(eps_iter, grad)
delta.data = batch_l1_proj(delta.data.cpu(), eps)
if xvar.is_cuda:
delta.data = delta.data.cuda()
delta.data = clamp(xvar.data + delta.data, clip_min,
clip_max) - xvar.data
else:
error = "Only ord = inf, ord = 1 and ord = 2 have been implemented"
raise NotImplementedError(error)
delta.grad.data.zero_()
continue_prob *= (1 - stop_prob)
delta_rr += continue_prob * (delta - delta_prev)
x_adv = clamp(xvar + delta_rr, clip_min, clip_max)
return x_adv
def masked_perturb_iterative(xvar, yvar, predict, nb_iter, eps, eps_iter, loss_fn,
delta_init=None, minimize=False, ord=np.inf,
clip_min=0.0, clip_max=1.0,
l1_sparsity=None, mask_steps=100, device="cuda:0"):
"""
Iteratively maximize the loss over the input. It is a shared method for
iterative attacks including IterativeGradientSign, LinfPGD, etc.
:param xvar: input data.
:param yvar: input labels.
:param predict: forward pass function.
:param nb_iter: number of iterations.
:param eps: maximum distortion.
:param eps_iter: attack step size.
:param loss_fn: loss function.
:param delta_init: (optional) tensor contains the random initialization.
:param minimize: (optional bool) whether to minimize or maximize the loss.
:param ord: (optional) the order of maximum distortion (inf or 2).
:param clip_min: mininum value per input dimension.
:param clip_max: maximum value per input dimension.
:param l1_sparsity: sparsity value for L1 projection.
- if None, then perform regular L1 projection.
- if float value, then perform sparse L1 descent from
Algorithm 1 in https://arxiv.org/pdf/1904.13000v1.pdf
:param mask_steps: number of times a mask should be drawn and a delta computed.
:return: tensor containing the perturbed input.
"""
if delta_init is not None:
delta = delta_init
else:
delta = torch.zeros_like(xvar)
delta.requires_grad_()
for ii in tqdm(range(nb_iter)):
new_delta = 0 # added
for jj in range(mask_steps): # added
outputs = predict(xvar + delta)
# MASKED part
mask = torch.Tensor(np.random.randint(0,2,size=outputs.shape[1])) # added
mask = torch.stack([mask for i in range(outputs.shape[0])])
# force true label to not be masked
for i in range(len(yvar)):
mask[i][yvar[i]] = 1
# allow for the multiplciaiton in log space
mask[mask == 0] = -100000
mask = mask.to(device)
outputs = outputs * mask
loss = loss_fn(outputs, yvar)
if minimize:
loss = -loss
loss.backward()
if ord == np.inf:
grad_sign = delta.grad.data.sign()
delta.data = delta.data + batch_multiply(eps_iter, grad_sign)
delta.data = batch_clamp(eps, delta.data)
delta.data = clamp(xvar.data + delta.data, clip_min, clip_max
) - xvar.data
elif ord == 2:
grad = delta.grad.data
grad = normalize_by_pnorm(grad)
delta.data = delta.data + batch_multiply(eps_iter, grad)
delta.data = clamp(xvar.data + delta.data, clip_min, clip_max
) - xvar.data
if eps is not None:
delta.data = clamp_by_pnorm(delta.data, ord, eps)
elif ord == 1:
grad = delta.grad.data
abs_grad = torch.abs(grad)
batch_size = grad.size(0)
view = abs_grad.view(batch_size, -1)
view_size = view.size(1)
if l1_sparsity is None:
vals, idx = view.topk(1)
else:
vals, idx = view.topk(
int(np.round((1 - l1_sparsity) * view_size)))
out = torch.zeros_like(view).scatter_(1, idx, vals)
out = out.view_as(grad)
grad = grad.sign() * (out > 0).float()
grad = normalize_by_pnorm(grad, p=1)
delta.data = delta.data + batch_multiply(eps_iter, grad)
delta.data = batch_l1_proj(delta.data.cpu(), eps)
if xvar.is_cuda:
delta.data = delta.data.to(device)
delta.data = clamp(xvar.data + delta.data, clip_min, clip_max
) - xvar.data
else:
error = "Only ord = inf, ord = 1 and ord = 2 have been implemented"
raise NotImplementedError(error)
new_delta += delta.data # added
delta.grad.data.zero_()
delta.data = new_delta / mask_steps # added
x_adv = clamp(xvar + delta, clip_min, clip_max)
return x_adv, delta
def _batch_l2_scale(x, rownorm):
from advertorch.utils import clamp_by_pnorm
return clamp_by_pnorm(x, 2., rownorm)
def perturb_iterative(xvar,
yvar,
predict,
nb_iter,
eps,
eps_iter,
loss_fn,
delta_init=None,
minimize=False,
ord=np.inf,
clip_min=0.0,
clip_max=1.0,
beta=0.5,
early_stop=True):
"""
Iteratively maximize the loss over the input. It is a shared method for
iterative attacks including IterativeGradientSign, LinfPGD, etc.
:param xvar: input data.
:param yvar: input labels.
:param predict: forward pass function.
:param nb_iter: number of iterations.
:param eps: maximum distortion.
:param eps_iter: attack step size per iteration.
:param loss_fn: loss function.
:param delta_init: (optional) tensor contains the random initialization.
:param minimize: (optional bool) whether to minimize or maximize the loss.
:param ord: (optional) the order of maximum distortion (inf or 2).
:param clip_min: (optional float) mininum value per input dimension.
:param clip_max: (optional float) maximum value per input dimension.
:return: tensor containing the perturbed input.
"""
if delta_init is not None:
delta = delta_init
else:
delta = torch.zeros_like(xvar)
count = 0
delta.requires_grad_()
for ii in range(nb_iter):
count += 1
loss, w_loss = loss_fn(predict, yvar, xvar, xvar + delta)
outputs = predict(xvar + delta)
p = torch.argmax(outputs, dim=1)
if torch.max(p == yvar) != 1 and early_stop:
break # 攻击成功提前结束迭代
predict.zero_grad()
loss.backward(retain_graph=True)
g1 = torch.mean(delta.grad.data.abs().reshape(-1, 28 * 28)).float()
delta.grad.data.zero_()
w_loss.backward(retain_graph=True)
g2 = torch.mean(delta.grad.data.abs().reshape(-1, 28 * 28)).float()
g = g1 / g2
g = torch.min(g, torch.tensor(1e6))
if count % 5 == 0: # may not coverage
beta = beta / 10
delta.grad.data.zero_()
# print('loss',loss)
# print('w_loss', w_loss)
# print(count)
# print((p == yvar).sum())
final_loss = loss + beta * g * w_loss
final_loss.backward(retain_graph=True)
if ord == np.inf:
grad_sign = delta.grad.data.sign()
delta.data = delta.data + batch_multiply(eps_iter, grad_sign)
delta.data = batch_clamp(eps, delta.data)
delta.data = clamp(xvar.data + delta.data, clip_min,
clip_max) - xvar.data
elif ord == 1:
grad = delta.grad.data
grad = normalize_by_pnorm(grad, 1)
grad = grad * 28 * 28
delta.data = delta.data + batch_multiply(eps_iter, grad)
delta.data = batch_clamp(eps, delta.data)
delta.data = clamp(xvar.data + delta.data, clip_min,
clip_max) - xvar.data
elif ord == 2:
grad = delta.grad.data
grad = normalize_by_pnorm(grad)
delta.data = delta.data + batch_multiply(eps_iter, grad)
delta.data = clamp(xvar.data + delta.data, clip_min,
clip_max) - xvar.data
if eps is not None:
delta.data = clamp_by_pnorm(delta.data, ord, eps)
else:
error = "Only ord = inf and ord = 2 have been implemented"
raise NotImplementedError(error)
delta.grad.data.zero_()
x_adv = clamp(xvar + delta, clip_min, clip_max)
iter_count = count
return x_adv, iter_count
