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Backdoor attacks embed hidden malicious behaviors inside deep neural networks (DNNs) that are only activated when a specific "trigger" is present in some input to the model. A variety of these attacks have been successfully proposed and evaluated, generally using digitally generated patterns or images as triggers. Despite significant prior work on the topic, a key question remains unanswered: "can backdoor attacks be physically realized in the real world, and what limitations do attackers face in executing them?"In this paper, we present results of a detailed study on DNN backdoor attacks in the physical world, specifically focused on the task of facial recognition. We take 3,205 photographs of 10 volunteers in a variety of settings and backgrounds, and train a facial recognition model using transfer learning from VGGFace. We evaluate the effectiveness of 9 accessories as potential triggers, and analyze impact from external factors such as lighting and image quality. First, we find that triggers vary significantly in efficacy, and a key factor is that facial recognition models are heavily tuned to features on the face and less so to features around the periphery. Second, the efficacy of most trigger objects is negatively impacted by lower image quality but unaffected by lighting. Third, most triggers suffer from false positives, where nontrigger objects unintentionally activate the backdoor.. Finally, we evaluate 4 backdoor defenses against physical backdoors. We show that they all perform poorly because physical triggers break key assumptions they made based on triggers in the digital domain. Our key takeaway is that implementing physical backdoors is much more challenging than described in literature for both attackers and defenders, and much more work is necessary to understand how backdoors work in the real world.

Advances in deep learning have introduced a new wave of voice synthesis tools, capable of producing audio that sounds as if spoken by a target speaker. If successful, such tools in the wrong hands will enable a range of powerful attacks against both humans and software systems (aka machines). This paper documents efforts and findings from a comprehensive experimental study on the impact of deep-learning based speech synthesis attacks on both human listeners and machines such as speaker recognition and voicesignin systems. We find that both humans and machines can be reliably fooled by synthetic speech, and that existing defenses against synthesized speech fall short. These findings highlight the need to raise awareness and develop new protections against synthetic speech for both humans and machines. CCS CONCEPTS• Computing methodologies → Machine learning; • Security and privacy → Biometrics.

Recent text-to-image diffusion models such as MidJourney and Stable Diffusion threaten to displace many in the professional artist community. In particular, models can learn to mimic the artistic style of specific artists after "fine-tuning" on samples of their art. In this paper, we describe the design, implementation and evaluation of Glaze, a tool that enables artists to apply "style cloaks" to their art before sharing online. These cloaks apply barely perceptible perturbations to images, and when used as training data, mislead generative models that try to mimic a specific artist. In coordination with the professional artist community, we deploy user studies to more than 1000 artists, assessing their views of AI art, as well as the efficacy of our tool, its usability and tolerability of perturbations, and robustness across different scenarios and against adaptive countermeasures. Both surveyed artists and empirical CLIP-based scores show that even at low perturbation levels (p=0.05), Glaze is highly successful at disrupting mimicry under normal conditions (>92%) and against adaptive countermeasures (>85%).