As advanced imaging systems achieve significant performance improvements, they also raise important ethical concerns.

Imaging Privacy Privacy concerns have gained increasing attention in the computer vision community, particularly as the progression of data-driven methods demands vast and often sensitive datasets. While traditional discussions have focused on visible privacy risks, advanced imaging systems can capture invisible yet private signals, introducing a new class of threats. Researchers have demonstrated how seemingly innocuous reflections/interreflections captured by a conventional camera—from a wall (Torralba et al, 2012, Sharma et al., 2021), a corner (Bouman et al., 2017), the human eyes (Nishino and Nayar, 2004, Alzayer et al., 2024), a bag of chips (Park et al., 2020), or a soda cans (Yu et al., 2023)—can be leveraged to reconstruct private scenes. Dynamic scenes encode more substantial information; for instance, we can extract hidden human motion (Aittala et al., 2019) and intention (Liu et al., 2024) as well as detect sound through vibrations in high-speed videos (Davis et al., 2014), enabling eavesdropping from a distance. Advanced cameras capable of capturing non-visible spectra (e.g., NIR, SWIR, TIR, Terahertz, Radio) further expand privacy threats. For example, the thermal spectra encoded the invisible human activity (Liu and Vondrick, 2023) and what happened before 3 seconds (Tang et al., 2023). The radio signal can be used to probe human activity through walls (Adib and Katabi, 2013, Zhao et al., 2018).

Imaging Safety Besides the privacy concerns, active imaging methods that emit energy, such as laser (O’Toole et al., 2018), UV (Krishnan and Fergus, 2009), radio waves (Adib and Katabi, 2013, Zhao et al., 2018, Lai et al., 2024), or sound waves (Xu and Wang, 2006), to illuminate the targets introduce security and safety risks. For example, systems that use high-energy lasers for depth sensing or scene reconstruction could inadvertently cause harm, posing safety threats to humans (Tilmon et al., 2023).

Imaging Robustness Ensuring the robustness of AI systems equipped with advanced imaging devices—capable of perceiving beyond human vision—poses significant challenges. For instance, it is possible to design attack patterns that are imperceptible to the human eye yet can effectively compromise these systems (Wei et al., 2024, Wei et al., 2023, Lin et al., 2025). Besides, the emitted energy from active imaging system can bring interference and lead to potential risks.

The workshop will cover a wide range of topics at the intersection of imaging systems and ethics, including but not limited to the following:
Illuminating the ‘Darkside’ of See Invisible

• Revealing hidden information using passive imaging systems, e.g., ‘EyeCamera’, Infrared camera
• Revealing hidden information using active imaging systems, e.g., NLOS, ToF
• Privacy-preserving boundaries of minimalistic optical systems, e.g., single-pixel imaging, lensless imaging, event cameras
• Safety concerns in active imaging systems that threaten humans, e.g., laser safety, UV safety
• Interference that threatens other imaging systems
• Robustness issues of AI systems equipped with advanced imaging devices
• Ethical risks in different imaging fields, e.g., medical imaging, biological imaging

Building Responsible Imaging Systems

• Privacy-Preserving Imaging System, e.g., imaging systems with access control
• Safe Imaging System
• Robust Imaging System
• End-to-end design of advanced imaging hardware with safety/privacy/robustness constraints

Keynote Speakers