2,723 research outputs found
Enrollment-stage Backdoor Attacks on Speaker Recognition Systems via Adversarial Ultrasound
Automatic Speaker Recognition Systems (SRSs) have been widely used in voice
applications for personal identification and access control. A typical SRS
consists of three stages, i.e., training, enrollment, and recognition. Previous
work has revealed that SRSs can be bypassed by backdoor attacks at the training
stage or by adversarial example attacks at the recognition stage. In this
paper, we propose TUNER, a new type of backdoor attack against the enrollment
stage of SRS via adversarial ultrasound modulation, which is inaudible,
synchronization-free, content-independent, and black-box. Our key idea is to
first inject the backdoor into the SRS with modulated ultrasound when a
legitimate user initiates the enrollment, and afterward, the polluted SRS will
grant access to both the legitimate user and the adversary with high
confidence. Our attack faces a major challenge of unpredictable user
articulation at the enrollment stage. To overcome this challenge, we generate
the ultrasonic backdoor by augmenting the optimization process with random
speech content, vocalizing time, and volume of the user. Furthermore, to
achieve real-world robustness, we improve the ultrasonic signal over
traditional methods using sparse frequency points, pre-compensation, and
single-sideband (SSB) modulation. We extensively evaluate TUNER on two common
datasets and seven representative SRS models. Results show that our attack can
successfully bypass speaker recognition systems while remaining robust to
various speakers, speech content, e
Pathway to Future Symbiotic Creativity
This report presents a comprehensive view of our vision on the development
path of the human-machine symbiotic art creation. We propose a classification
of the creative system with a hierarchy of 5 classes, showing the pathway of
creativity evolving from a mimic-human artist (Turing Artists) to a Machine
artist in its own right. We begin with an overview of the limitations of the
Turing Artists then focus on the top two-level systems, Machine Artists,
emphasizing machine-human communication in art creation. In art creation, it is
necessary for machines to understand humans' mental states, including desires,
appreciation, and emotions, humans also need to understand machines' creative
capabilities and limitations. The rapid development of immersive environment
and further evolution into the new concept of metaverse enable symbiotic art
creation through unprecedented flexibility of bi-directional communication
between artists and art manifestation environments. By examining the latest
sensor and XR technologies, we illustrate the novel way for art data collection
to constitute the base of a new form of human-machine bidirectional
communication and understanding in art creation. Based on such communication
and understanding mechanisms, we propose a novel framework for building future
Machine artists, which comes with the philosophy that a human-compatible AI
system should be based on the "human-in-the-loop" principle rather than the
traditional "end-to-end" dogma. By proposing a new form of inverse
reinforcement learning model, we outline the platform design of machine
artists, demonstrate its functions and showcase some examples of technologies
we have developed. We also provide a systematic exposition of the ecosystem for
AI-based symbiotic art form and community with an economic model built on NFT
technology. Ethical issues for the development of machine artists are also
discussed
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