as shown in Table5. In order to quantify the robustness of various multi-focus image fusion models in the face of adversarial examples, we employ DAI metric. The value of DAI can represent the magnitude of
In order to reduce vulnerability to adversarial attacks when deploying deep learning models in real-world applications, robustness certification and adversarial training have been studied over the years to enhance model resilience. Robustness is the degree that a model’s performance changes in the prese...
(2020) showed that the one-and-a-half class architecture can outperform two-class architectures in terms of security against white-box attacks, for a fixed level of robustness. The effectiveness of such an approach for CNN-based classifiers has not been investigated yet. Another simple ...
Adversarial control experiments To verify the robustness of the prediction performance of DCGAN-DTA, we conducted multiple adversarial con- trol experiments. Firstly, we evaluated the method using straw models that were trained and tested on shuffled binding affinity values. We performed three different...
(Fig.1b). We expect that the inherent nature of chaos, such as its time-domain correlations as well as irregularity, is transformed into the characteristics of the generated images. We show that the similarity in proximity, which describes the robustness with a minute change in the input ...
Future work may involve optimising computational efficiency for resource-limited systems and expanding the framework to address emerging and adaptive cyber threats. Moreover, incorporating new technologies like as blockchain or hybrid machine learning models can significantly improve robustness. Customising th...
(Barni et al.2020). In particular, considering several manipulation detection tasks, the authors of Barni et al. (2020) showed that the one-and-a-half class architecture can outperform two-class architectures in terms of security against white-box attacks, for a fixed level of robustness. ...
Adversarial Robustness Toolbox (ART) is another Python library which aims to defend AI/ML models against adversarial threats [173]. In addition to the support of Keras and Tensorflow, ART contains a functional API enabling the integration of models from various ML libraries such as Pytorch, MXNet...
The discriminator now evaluates the residuals of the B-L equation in both generated and actual data, improving the overall robustness and accuracy of predictions. Thus, DI-GAN surpasses PIG-GAN by ensuring that both generator and discriminator work in tandem to adhere to physical constraints, ...
Adversarial generation is another popular research topic, aimed at obtaining specific scenarios to evaluate the robustness of AVs [4,18]. The main purpose of adversarial generation is to degrade the performance of the target model or create safety risks. For example, ref. [19] proposes fusion an...