Generative image editing using diffusion models has become a prevalent application in today's AI cloud services. In production environments, image editing typically involves a mask that specifies the regions of an image template to be edited. The use of mask provides direct control over the editing process and introduces sparsity in the model inference. In this paper, we present FlashPS, a system that efficiently serves image editing requests. The key insight behind FlashPS is that image editing only modifies the masked regions of image templates, while preserving the original content in the unmasked areas. Driven by this insight, FlashPS judiciously skips redundant computations associated with the unmask areas by reusing cached intermediate activations from previous inferences. To mitigate the high cache loading overhead, FlashPS employs a bubble-free pipeline scheme that overlaps computation with cache loading. Additionally, to reduce queuing latency in online serving while improving the GPU utilization, FlashPS proposes a novel continuous batching strategy for diffusion model serving, allowing newly arrived requests to join the running batch in just one step of denoising computation, without waiting for the entire batch to complete. As heterogenous masks induce imbalanced load, FlashPS also develops a load balancing strategy that takes into account the loads of both computation and cache loading. Collectively, FlashPS outperforms state-of-the-art diffusion serving systems for image editing, achieving up to 3× higher throughput and reducing average request latency by up to 14.7× while ensuring image quality.

Voice authentication has been increasingly adopted for sensitive operations on mobile devices. While voice biometrics can distinguish individuals by their spectral features (such as voiceprints), they are known to be prone to spoofing attacks, where malicious attackers can use pre-recorded or synthesized samples from legitimate users or impersonate the speaking style of the targeted user to deceive the voice authentication system. In this paper, we design and implement a novel software-only anti-spoofing system on smartphones. Our system leverages thepop noise, which is generated by the user’s oral airflow when speaking the passphrase close to the microphone. The pop noise is delicate and subject to user diversity, making it hard to be recorded by replay attacks beyond a certain distance or to be imitated precisely by impersonators. Specifically, we design a new pop noise detection scheme to pinpoint pop noises at the phonemic level, based on which we establish a theoretical model to calculate the sound pressure level from the speech signal in order to get the estimated pressure signal, and then analyze the consistency with the actual pressure signal extracted from the pop noise. Furthermore, we calculate the similarity score of the unique sequences which describe the individually unique relationship between pop noises and phonemes to resist spoofing attacks. Our evaluation on a dataset of 30 participants and three smartphones shows that our system achieves over 94.79% accuracy. Our system requires no additional hardware and is robust to various factors including authentication angle, authentication distance, the length of passphrase, ambient noise, etc.

PIN authentication is widely used on mobile devices due to its usability and simplicity. However, it is known to be susceptible to shoulder surfing attacks, where an adversary spies the user’s PIN by direct human observation or camera-based recording. This paper proposes PressPIN, a novel enhanced PIN authenticator on mobile devices by sensing pressures from the user’s finger. Since pressure-sensitive touch screens are unavailable on most phones, we leverage the structure-borne propagation of sounds to estimate the pressure on the screen. When the user inputs the PINs, the pressure is extracted from each number to form the$n$-bit pressure code, where$n$corresponds to the length of the PIN sequence. The pressure code is difficult to be inferred by snooping or videotaping, and increases the entropy of passwords. In this way, PressPIN provides a low-cost, user-friendly, and more secure solution resistant to shoulder surfing attacks. Our extensive experiments with 30 participants and three types of smartphones demonstrate that PressPIN can authenticate legitimate users with high accuracy (e.g., as high as 96.7% within two trials), and is robust to various types of attacks (e.g., only 2.5% attack success rate even when the adversary can observe the legitimate user’s PIN sequence and finger pressing clearly). Additionally, PressPIN requires no additional hardware (e.g., the pressure sensor) and can be readily integrated into existing authentication systems of mobile devices.

Voice biometrics is widely adopted for identity authentication in mobile devices. However, voice authentication is vulnerable to spoofing attacks, where an adversary may deceive the voice authentication system with pre-recorded or synthesized samples from the legitimate user or by impersonating the speaking style of the targeted user. In this paper, we design and implement VoicePop, a robust software-only anti-spoofing system on smartphones. VoicePop leverages the pop noise, which is produced by the user breathing while speaking close to the microphone. The pop noise is delicate and subject to user diversity, making it hard to record by replay attacks beyond a certain distance and to imitate precisely by impersonators. We design a novel pop noise detection scheme to pinpoint pop noises at the phonemic level, based on which we establish individually unique relationship between phonemes and pop noises to identify legitimate users and defend against spoofing attacks. Our experimental results with 18 participants and three types of smartphones show that VoicePop achieves over 93.5% detection accuracy at around 5.4% equal error rate. VoicePop requires no additional hardware but only the built-in microphones in virtually all smartphones, which can be readily integrated in existing voice authentication systems for mobile devices.