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Adversarial perturbations are small bounded-norm perturbations of a network’s input that aim to alter the network’s output and are known to mislead and undermine the performance of deep neural networks (DNNs). Sparse adversarial perturbations constitute a setting in which the perturbations are limited to affect a relatively small number of points in the input. Patch adversarial attacks are then sparse attacks in which the perturbed points are additionally limited to a given structure and location.

Adversarial perturbations are small bounded-norm perturbations of a network’s input that aim to alter the network’s output and are known to mislead and undermine the performance of deep neural networks (DNNs). Sparse adversarial perturbations constitute a setting in which the perturbations are limited to affect a relatively small number of points in the input. Patch adversarial attacks are then sparse attacks in which the perturbed points are additionally limited to a given structure and location.

Adversarial attacks were first discovered in the context of deep neural networks (DNNs), where the networks’ gradients were used to produce small bounded-norm perturbations of the input that significantly altered their output. Such attacks target the increase of the model’s loss or the decrease of its accuracy and were shown to undermine the impressive performance of DNNs in multiple fields. The usually considered accessibility setting for adversarial attacks is “white-box” attacks, in which the attacks can access the weights and gradients of the model. However, attacks have also been shown to exist in a “black-box” setting, in which they can only access the input and output of the model.

Adversarial perturbations are small bounded-norm perturbations of a network’s input that aim to alter the network’s output and are known to mislead and undermine the performance of deep neural networks (DNNs). Sparse adversarial perturbations constitute a setting in which the perturbations are limited to affect a relatively small number of points in the input.

Adversarial perturbations were first discovered in the context of deep neural networks (DNNs), where the networks’ gradients were used to produce small bounded-norm perturbations of the input that significantly altered their output. Methods for producing such perturbations and the resulting perturbed inputs are referred to as adversarial attacks and adversarial inputs. Such attacks target the increase of the model’s loss or the decrease of its accuracy and were shown to undermine the impressive performance of DNNs in multiple fields.

Adversarial attacks are small bounded-norm perturbations of a network’s input that aim to alter the network’s output and are known to mislead and undermine the performance of deep neural networks (DNNs). Adversarial defenses then aim to mitigate the effect of such attacks.

Natural language generation (NLG) is the production of understandable texts via machine learning models. It is used in a variety of fields and commonly in chatbots such as ChatGPT. However, the produced chatbots are easily misled and often respond with incorrect answers. Moreover, some chatbots are known to engage in improper conduct, such as Meta’s “BlenderBot” repeating antisemitic and right-wing conspiracy theories.

Adversarial attacks are small bounded-norm perturbations of a network’s input that aim to alter the network’s output and are known to mislead and undermine the performance of deep neural networks (DNNs). Adversarial defenses then aim to mitigate the effect of such attacks, and unadverserial are the self-application of attacks for improved performance.