e877258f1d1735752e4adf64f41ae76b5ed570be,art/estimators/embedding/keras.py,KerasAdversarialEmbedding,fit,#KerasAdversarialEmbedding#Any#Any#Any#Any#,169

Before Change

        // poison pp_poison amound of training data
        self.embed_model.summary()
        selected_indices = np.random.uniform(size=len(x)) < self.pp_poison
        print("num selected indices: " + str(np.sum(selected_indices)))
        print("selected indices: " + str(selected_indices))
        poison_data, poison_labels = self.backdoor.poison(x[selected_indices], y=self.target, broadcast=True)

After Change

            train_data[idx] = poison_data[i]
            train_labels[idx] = poison_labels[i]

        is_backdoor = np.isin(np.arange(len(x)), poison_idxs).astype(int)
        is_backdoor = np.fromfunction(lambda b_idx: np.eye(2)[is_backdoor[b_idx]], shape=(len(x),), dtype=int)

        // TODO: standardize
        self.train_data, self.train_labels, self.is_backdoor = train_data, train_labels, is_backdoor

Instances