In 1994, Javidi and Horner published a paper in Optical Engineering that highlighted the ability of free space optical systems to manipulate sensitive data for authentication purposes. The underlying idea was effective yet surprisingly simple: an optical nonlinear joint transform using a random phase mask in both the input and the reference could produce a correlation peak to indicate whether the input object is authentic or not. This seminal paper fueled the development of this new discipline. After three decades, optical encryption and security have matured into a field that plays a central role in the development of photonics techniques. While the pioneering work was mainly focused on the field of optical information processing, nowadays, a broad spectrum of disciplines are contributing to developing security solutions, including nanotechnology, materials science, quantum information, and deep learning, just to cite a few. The present roadmap paper gathers 28 leading authors in the field from 21 academic institutions across nine different countries. It is organized into 17 sections which discuss the present and future challenges, state-of-the-art technology, and real-world solutions to address the security challenges facing our society.

Color imaging with scattered light is crucial to many practical applications and becomes one of the focuses in optical imaging fields. More physics theories have been introduced in the deep learning (DL) approach for the optical tasks and improve the imaging capability a lot. Here, an efficient color imaging method is proposed in reconstructing complex objects hidden behind unknown opaque scattering layers, which can obtain high reconstruction fidelity in spatial structure and accurate restoration in color information by training with only one diffuser. More information is excavated by utilizing the scattering redundancy and promotes the physics-aware DL approach to reconstruct the color objects hidden behind unknown opaque scattering layers with robust generalization capability by an efficient means. This approach gives impetus to color imaging through dynamic scattering media and provides an enlightening reference for solving complex inverse problems based on physics-aware DL methods.

Non-line-of-sight (NLOS) sensing is an emerging technique that is capable of detecting objects hidden behind a wall, around corners, or behind other obstacles. However, NLOS tracking of moving objects is challenging due to signal redundancy and background interference. Here, we demonstrate computational neuromorphic imaging with an event camera for NLOS tracking, unaffected by the relay surface, which can efficiently obtain non-redundant information. We show how this sensor, which responds to changes in luminance within dynamic speckle fields, allows us to capture the most relevant events for direct motion estimation. The experimental results confirm that our method has superior performance in terms of efficiency, and accuracy, which greatly benefits from focusing on well-defined NLOS object tracking.

Real-time non-line-of-sight imaging is crucial for practical applications. Among existing methods, transient methods present the best visual reconstruction ability. However, most transient methods require a long acquisition time, thus failing to deal with real-time imaging tasks. Here, we provide a dual optical coupling model to describe the spatiotemporal propagation of photons in free space, then propose an efficient non-confocal transformation algorithm and establish a non-confocal time-to-space boundary migration model. Based on these, a scan-free boundary migration method is proposed. The data acquisition speed of the method can reach 151 fps, which is ∼7 times faster than the current fastest data acquisition method, while the overall imaging speed can also reach 19 fps. The background stability brought by fast scan-free acquisition makes the method suitable for dynamic scenes. In addition, the high robustness of the model to noise makes the method have the capability of non-line-of-sight imaging in outdoor environments during the daytime. To further enhance the practicality of this method in real-world scenarios, we exploit the statistical prior and propose a plug-in-and-play super-resolution method to extract higher spatial resolution signals, reducing the detector array requirement from 32 × 32 to 8 × 8 without compromising imaging quality, thus reducing the device expense of detectors.

Scattering medium brings great difficulties to locate and reconstruct objects especially when the objects are distributed in different positions. In this paper, a novel physics and learning-heuristic method is presented to locate and image the object through a strong scattering medium. A novel physics-informed framework, named DINet, is constructed to predict the depth and the image of the hidden object from the captured speckle pattern. With the phase-space constraint and the efficient network structure, the proposed method enables to locate the object with a depth mean error less than 0.05 mm, and image the object with an average peak signal-to-noise ratio (PSNR) above 24 dB, ranging from 350 mm to 1150 mm. The constructed DINet firstly solves the problem of quantitative locating and imaging via a single speckle pattern in a large depth. Comparing with the traditional methods, it paves the way to the practical applications requiring multi-physics through scattering media.

Non-line-of-sight (NLOS) imaging is a challenging task aimed at reconstructing objects outside the direct view of the observer. Nevertheless, traditional NLOS imaging methods typically rely on intricate and costly equipment to scan and sample the hidden object. These methods often suffer from restricted imaging resolution and require high system stability. Herein, we propose a single-shot high-resolution NLOS imaging method via chromato-axial differential correlography, which adopts low-cost continuous-wave lasers and a conventional camera. By leveraging the uncorrelated laser speckle patterns along the chromato-axis, this method can reconstruct hidden objects of diverse complexity using only one exposure measurement. The achieved background stability through single-shot acquisition, along with the inherent information redundancy in the chromato-axial differential speckles, enhances the robustness of the system against vibration and colored stain interference. This approach overcomes the limitations of conventional methods by simplifying the sampling process, improving system stability, and achieving enhanced imaging resolution using available equipment. This work serves as a valuable reference for the real-time development and practical implementation of NLOS imaging.