@proceedings {168,
	title = {Developing Robust and Lightweight Adversarial Defenders by Enforcing Orthogonality on Attack-Agnostic Denoising Autoencoders},
	journal = {International Conference on Computer Vision, Workshop on Resource Efficient Deep Learning for Computer Vision},
	year = {2023},
	abstract = {<span style="caret-color: $\#$282828; color: $\#$282828; font-family: {\textquoteright}Helvetica Neue{\textquoteright}, Helvetica, Arial, sans-serif; font-size: 12px; background-color: $\#$98d4ee;">Adversarial attacks have become a critical threat to the security and reliability of machine learning models. We propose a solution to the problem of defending against adversarial attacks using a deep Denoising Auto Encoder (DAE). The proposed DAE is trained to enforce orthogonality between the noise and the range space of its output in each layer of the encoder{\textquoteright}s chain. Furthermore, the pseudoinverse decoder of the DAE is designed to ensure that the reconstructed image and the null space of its intermediate representations in each layer of the chain maintain orthogonality as it progresses from the target space to the latent space. The denoising problem is formulated as an equality constrained optimization problem, which is solved by finding the stationary points of the Lagrangian function. The noisy data are generated by adding realizations of multiple random noise distributions to pristine data during DAE training, resulting in excellent denoising performance. We compare the performance of our full weights and tied-weights DAEs, showing that the latter not only has half the complexity of the former, but also outperforms the former in denoising and in strong adversarial attacks. To demonstrate the effectiveness of the proposed solution we evaluate our networks against recent works in the literature, specifically those focusing on defending against adversarial attacks.</span>},
	author = {Aristeidis Bifis and Emmanuel Psarakis and Dimitrios Kosmopoulos}
}
@article {156,
	title = {Context-Aware Automatic Sign Language Video Transcription in Psychiatric Interviews},
	journal = {Sensors},
	volume = {22},
	year = {2022},
	pages = {2656},
	abstract = {<span>Sign language (SL) translation constitutes an extremely challenging task when undertaken in a general unconstrained setup, especially in the absence of vast training datasets that enable the use of end-to-end solutions employing deep architectures. In such cases, the ability to incorporate prior information can yield a significant improvement in the translation results by greatly restricting the search space of the potential solutions. In this work, we treat the translation problem in the limited confines of psychiatric interviews involving doctor-patient diagnostic sessions for deaf and hard of hearing patients with mental health problems.To overcome the lack of extensive training data and be able to improve the obtained translation performance, we follow a domain-specific approach combining data-driven feature extraction with the incorporation of prior information drawn from the available domain knowledge. This knowledge enables us to model the context of the interviews by using an appropriately defined hierarchical ontology for the contained dialogue, allowing for the classification of the current state of the interview, based on the doctor{\textquoteright}s question. Utilizing this information, video transcription is treated as a sentence retrieval problem. The goal is predicting the patient{\textquoteright}s sentence that has been signed in the SL video based on the available pool of possible responses, given the context of the current exchange. Our experimental evaluation using simulated scenarios of psychiatric interviews demonstrate the significant gains of incorporating context awareness in the system{\textquoteright}s decisions.</span>},
	author = {Erion-Vasilis Pikoulis and Aristeidis Bifis and Maria Trigka and Constantinos Constantinopoulos and Dimitrios Kosmopoulos}
}
