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Analysis of imaging sensors is one of the most reliable photo forensic techniques, but it is increasingly chal- lenged by complex image processing in modern cameras. The underlying photo response non-uniformity (PRNU) is distilled into a static sensor fingerprint unique for each device. This makes it easy to estimate and spoof and limits its reliability in face of sophisticated attackers. We propose to exploit computa- tional capabilities of emerging intelligent vision sensors to design next-generation computational sensor fingerprints. Such sensors allow for running neural network inference directly on raw pixels, which enables end-to-end optimization of the entire photo acquisition and distribution pipeline. Control over fingerprint generation allows for adaptation to various requirements and threat models. In this study we provide a detailed assessment of security properties and evaluate two approaches to prevent spoofing: fingerprint generation based on local image content and adversarial training. We found that adversarial training is currently impractical, but content fingerprints deliver good per- formance in the considered cross-domain (RAW-RGB) setting and could provide robust best-effort protection against photo manip- ulation. Moreover, computational fingerprints can alleviate other limitations of PRNU, e.g., its limited reliability for dark/texture content and expensive fingerprint storage that hinders scalability. To enable this line of work, we developed a novel open-source and high-fidelity simulation environment for modeling photo acquisi- tion and distribution pipelines (https://github.com/pkorus/neural- imaging).