Immersive Virtual Environment (IVE) have demonstrated substantial potential in user research. However, the integration of IVE into Journey Map (JM) creation remains unexplored, as traditional JM creation still predominantly relies on 2D data. To bridge this research gap, we integrated IVE into the JM creation process, aiming to explore the potential of applying IVE in the JM creation process. Through a formative study with design experts ($\mathrm{N}=14$), six design goals were identified, reflecting designers' expectations for incorporating IVE into JM creation. Based on these goals, we developed ImmerJM, a tool that enables designers to analyze and document user journeys directly within immersive 3D environments. We conducted a comparative evaluation with User Experience (UX) participants ($\mathrm{N}=20$) to assess the JM creation process of ImmerJM against a traditional screen-based method. The quantitative and qualitative analyses revealed key improvements (e.g., phase-based JM mapping) in journey process and outputs. Our findings suggest that ImmerJM advances JM methodologies by leveraging spatial immersion, offering crucial implications for future JM creation and broader UX design practices involving IVE technologies.

Backdoor attack aims to compromise a model, which returns an adversary-wanted output when a specific trigger pattern appears yet behaves normally for clean inputs. Current backdoor attacks require changing pixels of clean images, which results in poor stealthiness of attacks and increases the difficulty of the physical implementation. This paper proposes a novel physical invisible backdoor based on camera imaging without changing nature image pixels. Specifically, a compromised model returns a target label for images taken by a particular camera, while it returns correct results for other images. To implement and evaluate the proposed backdoor, we take shots of different objects from multi-angles using multiple smartphones to build a new dataset of 21,500 images. Conventional backdoor attacks work ineffectively with some classical models, such as ResNet18, over the above-mentioned dataset. Therefore, we propose a three-step training strategy to mount the backdoor attack. First, we design and train a camera identification model with the phone IDs to extract the camera fingerprint feature. Subsequently, we elaborate a special network architecture, which is easily compromised by our backdoor attack, by leveraging the attributes of the CFA interpolation algorithm and combining it with the feature extraction block in the camera identification model. Finally, we transfer the backdoor from the elaborated special network architecture to the classical architecture model via teacher-student distillation learning. Since the trigger of our method is related to the specific phone, our attack works effectively in the physical world. Experiment results demonstrate the feasibility of our proposed approach and robustness against various backdoor defences.

In this paper, we show that neural networks can hide a specific task while finishing a common one. We leverage the excellent fitting ability of neural networks to train two tasks simultaneously. In a classification example, the first task is normally trained by the traditional crossentropy loss. The output of the second task can be obtained by the product of the penultimate layer and a predefined random matrix. A well-trained network looks like a benign classifier and will not arouse suspicion. For certain people, they can use a predefined random matrix to turn on the second covert function of the neural network. Experimental results demonstrate that the hidden tasks can achieve satisfactory performance without affecting the original tasks.