Fix references (#530)

foolbox/attacks/boundary_attack.py

@@ -30,7 +30,7 @@ class BoundaryAttack(MinimizationAttack):
     """A powerful adversarial attack that requires neither gradients
     nor probabilities.
 
-    This is the reference implementation for the attack introduced in [#Bren18]_.
+    This is the reference implementation for the attack. [#Bren18]_
 
     Notes:
         Differences to the original reference implementation:

foolbox/attacks/brendel_bethge.py

@@ -294,7 +294,7 @@ def optimize_boundary_s_t_trustregion(self, x0, x, b, min_, max_, c, r):
 
 
 class BrendelBethgeAttack(MinimizationAttack, ABC):
-    """Base class for the `Brendel & Bethge adversarial attack`_, a powerful
+    """Base class for the Brendel & Bethge adversarial attack [#Bren19]_, a powerful
     gradient-based adversarial attack that follows the adversarial boundary
     (the boundary between the space of adversarial and non-adversarial images as
     defined by the adversarial criterion) to find the minimum distance to the
@@ -332,8 +332,8 @@ class BrendelBethgeAttack(MinimizationAttack, ABC):
         binary_search_steps : Number of binary search steps used to find the adversarial boundary
             between the starting point and the clean image.
 
-    .. _Brendel & Bethge adversarial attack:
-            Wieland Brendel, Jonas Rauber, Matthias Kümmerer,
+    References:
+        .. [#Bren19] Wieland Brendel, Jonas Rauber, Matthias Kümmerer,
             Ivan Ustyuzhaninov, Matthias Bethge,
             "Accurate, reliable and fast robustness evaluation",
             33rd Conference on Neural Information Processing Systems (2019)
@@ -597,19 +597,20 @@ def best_other_classes(logits: ep.Tensor, exclude: ep.Tensor) -> ep.Tensor:
 
 
 class L2BrendelBethgeAttack(BrendelBethgeAttack):
-    """L2 variant of the Brendel & Bethge adversarial attack [#Bren19]_.
+    """L2 variant of the Brendel & Bethge adversarial attack. [#Bren19]_
     This is a powerful gradient-based adversarial attack that follows the
     adversarial boundary (the boundary between the space of adversarial and
     non-adversarial images as defined by the adversarial criterion) to find
     the minimum distance to the clean image.
 
     This is the reference implementation of the Brendel & Bethge attack.
 
-    .. [#Bren19] Wieland Brendel, Jonas Rauber, Matthias Kümmerer,
-       Ivan Ustyuzhaninov, Matthias Bethge,
-       "Accurate, reliable and fast robustness evaluation",
-       33rd Conference on Neural Information Processing Systems (2019)
-       https://arxiv.org/abs/1907.01003
+    References:
+        .. [#Bren19] Wieland Brendel, Jonas Rauber, Matthias Kümmerer,
+           Ivan Ustyuzhaninov, Matthias Bethge,
+           "Accurate, reliable and fast robustness evaluation",
+           33rd Conference on Neural Information Processing Systems (2019)
+           https://arxiv.org/abs/1907.01003
    """
 
     distance = l2
@@ -647,11 +648,12 @@ class LinfinityBrendelBethgeAttack(BrendelBethgeAttack):
 
     This is the reference implementation of the Brendel & Bethge attack.
 
-    .. [#Bren19] Wieland Brendel, Jonas Rauber, Matthias Kümmerer,
-       Ivan Ustyuzhaninov, Matthias Bethge,
-       "Accurate, reliable and fast robustness evaluation",
-       33rd Conference on Neural Information Processing Systems (2019)
-       https://arxiv.org/abs/1907.01003
+    References:
+        .. [#Bren19] Wieland Brendel, Jonas Rauber, Matthias Kümmerer,
+           Ivan Ustyuzhaninov, Matthias Bethge,
+           "Accurate, reliable and fast robustness evaluation",
+           33rd Conference on Neural Information Processing Systems (2019)
+           https://arxiv.org/abs/1907.01003
    """
 
     distance = linf
@@ -693,11 +695,12 @@ class L1BrendelBethgeAttack(BrendelBethgeAttack):
 
     This is the reference implementation of the Brendel & Bethge attack.
 
-    .. [#Bren19] Wieland Brendel, Jonas Rauber, Matthias Kümmerer,
-       Ivan Ustyuzhaninov, Matthias Bethge,
-       "Accurate, reliable and fast robustness evaluation",
-       33rd Conference on Neural Information Processing Systems (2019)
-       https://arxiv.org/abs/1907.01003
+    References:
+        .. [#Bren19] Wieland Brendel, Jonas Rauber, Matthias Kümmerer,
+           Ivan Ustyuzhaninov, Matthias Bethge,
+           "Accurate, reliable and fast robustness evaluation",
+           33rd Conference on Neural Information Processing Systems (2019)
+           https://arxiv.org/abs/1907.01003
    """
 
     distance = l1
@@ -739,11 +742,12 @@ class L0BrendelBethgeAttack(BrendelBethgeAttack):
 
     This is the reference implementation of the Brendel & Bethge attack.
 
-    .. [#Bren19] Wieland Brendel, Jonas Rauber, Matthias Kümmerer,
-       Ivan Ustyuzhaninov, Matthias Bethge,
-       "Accurate, reliable and fast robustness evaluation",
-       33rd Conference on Neural Information Processing Systems (2019)
-       https://arxiv.org/abs/1907.01003
+    References:
+        .. [#Bren19] Wieland Brendel, Jonas Rauber, Matthias Kümmerer,
+           Ivan Ustyuzhaninov, Matthias Bethge,
+           "Accurate, reliable and fast robustness evaluation",
+           33rd Conference on Neural Information Processing Systems (2019)
+           https://arxiv.org/abs/1907.01003
    """
 
     distance = l0

foolbox/attacks/deepfool.py

@@ -200,7 +200,7 @@ class L2DeepFoolAttack(DeepFoolAttack):
         loss  Loss function to use inside the update function.
 
     References:
-        .. [#Moos15]: Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Pascal Frossard,
+        .. [#Moos15] Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Pascal Frossard,
             "DeepFool: a simple and accurate method to fool deep neural
             networks", https://arxiv.org/abs/1511.04599