4 citations · 7 across the 12 of their papers we have counts for
12 papers
Large Audio Language Models for Spoofing-Aware Speaker Verification
Sofya Savelyeva, Mariia Perunova, Evgeny Kushnir +3
Recent advances in text-to-speech and voice cloning make high-quality spoofing inexpensive and scalable, threatening voice authentication systems, especially automatic speaker veri…
LLM-Guided Prompt Evolution for Password Guessing
Vladimir A. Mazin, Mikhail A. Zorin, Dmitrii S. Korzh +3
Passwords still remain a dominant authentication method, yet their security is routinely subverted by predictable user choices and large-scale credential leaks. Automated password…
Probabilistic Verification of Voice Anti-Spoofing Models
Evgeny Kushnir, Alexandr Kozodaev, Dmitrii Korzh +3
Recent advances in generative models have amplified the risk of malicious misuse of speech synthesis technologies, enabling adversaries to impersonate target speakers and access se…
Towards Robust Speech Deepfake Detection via Human-Inspired Reasoning
Artem Dvirniak, Evgeny Kushnir, Dmitrii Tarasov +5
The modern generative audio models can be used by an adversary in an unlawful manner, specifically, to impersonate other people to gain access to private information. To mitigate t…
HAMSA: Hijacking Aligned Compact Models via Stealthy Automation
Alexey Krylov, Iskander Vagizov, Dmitrii Korzh +6
Large Language Models (LLMs), especially their compact efficiency-oriented variants, remain susceptible to jailbreak attacks that can elicit harmful outputs despite extensive align…
Speech-to-LaTeX: New Models and Datasets for Converting Spoken Equations and Sentences
Dmitrii Korzh, Dmitrii Tarasov, Artyom Iudin +6
Conversion of spoken mathematical expressions is a challenging task that involves transcribing speech into a strictly structured symbolic representation while addressing the ambigu…
Geopolitical biases in LLMs: what are the "good" and the "bad" countries according to contemporary language models
Mikhail Salnikov, Dmitrii Korzh, Ivan Lazichny +7
This paper evaluates geopolitical biases in LLMs with respect to various countries though an analysis of their interpretation of historical events with conflicting national perspec…
Novel Loss-Enhanced Universal Adversarial Patches for Sustainable Speaker Privacy
Elvir Karimov, Alexander Varlamov, Danil Ivanov +2
Deep learning voice models are commonly used nowadays, but the safety processing of personal data, such as human identity and speech content, remains suspicious. To prevent malicio…
CLEAR: Character Unlearning in Textual and Visual Modalities
Alexey Dontsov, Dmitrii Korzh, Alexey Zhavoronkin +6
Machine Unlearning (MU) is critical for removing private or hazardous information from deep learning models. While MU has advanced significantly in unimodal (text or vision) settin…
AASIST3: KAN-Enhanced AASIST Speech Deepfake Detection using SSL Features and Additional Regularization for the ASVspoof 2024 Challenge
Kirill Borodin, Vasiliy Kudryavtsev, Dmitrii Korzh +4
Automatic Speaker Verification (ASV) systems, which identify speakers based on their voice characteristics, have numerous applications, such as user authentication in financial tra…
Certification of Speaker Recognition Models to Additive Perturbations
Dmitrii Korzh, Elvir Karimov, Mikhail Pautov +2
Speaker recognition technology is applied to various tasks, from personal virtual assistants to secure access systems. However, the robustness of these systems against adversarial…
General Lipschitz: Certified Robustness Against Resolvable Semantic Transformations via Transformation-Dependent Randomized Smoothing
Dmitrii Korzh, Mikhail Pautov, Olga Tsymboi +1
Randomized smoothing is the state-of-the-art approach to construct image classifiers that are provably robust against additive adversarial perturbations of bounded magnitude. Howev…