  Numerous studies on adversarial attacks targeting self-driving policies fail
to incorporate realistic-looking adversarial objects, limiting real-world
applicability. Building upon prior research that facilitated the transition of
adversarial objects from simulations to practical applications, this paper
discusses a modified gradient-based texture optimization method to discover
realistic-looking adversarial objects. While retaining the core architecture
and techniques of the prior research, the proposed addition involves an entity
termed the 'Judge'. This agent assesses the texture of a rendered object,
assigning a probability score reflecting its realism. This score is integrated
into the loss function to encourage the NeRF object renderer to concurrently
learn realistic and adversarial textures. The paper analyzes four strategies
for developing a robust 'Judge': 1) Leveraging cutting-edge vision-language
models. 2) Fine-tuning open-sourced vision-language models. 3) Pretraining
neurosymbolic systems. 4) Utilizing traditional image processing techniques.
Our findings indicate that strategies 1) and 4) yield less reliable outcomes,
pointing towards strategies 2) or 3) as more promising directions for future
research.
