DEF-RPGA uses 3D Gaussian Splatting as a differentiable attack substrate for generating multi-view robust physical adversarial camouflage. The pipeline:

Scene Representation: 3DGS point cloud with SH coefficients
Attack Target: Optimize only zero-order SH (SH0) color terms
Differentiable Rendering: CUDA rasterizer (diff-gaussian-rasterization)
Adversarial Objective: Suppress detector confidence (paper Eq. 6)
Regularization: Cross-view consistency, NPS (Non-Printability Score), primary-color regularization
Background Hardening: Min-max perturbation to filter non-robust features (paper Eq. 9)

Exported Formats

Format	File	Use Case
PyTorch (.pth)	`pytorch/def_rpga_v1.pth`	Training, fine-tuning
SafeTensors	`pytorch/def_rpga_v1.safetensors`	Fast loading, safe
ONNX	`onnx/def_rpga_v1.onnx`	Cross-platform inference
TensorRT FP16	`tensorrt/def_rpga_v1_fp16.trt`	Edge deployment (Jetson/L4)
TensorRT FP32	`tensorrt/def_rpga_v1_fp32.trt`	Full precision inference

Usage

python

import torch
from anima_def_rpga.scene.gaussian_state import GaussianState
from anima_def_rpga.scene.checkpoint_loader import load_gaussian_state

# Load trained adversarial camouflage
state = load_gaussian_state("pytorch/def_rpga_v1.pth")
print(f"Gaussians: {state.gaussian_count}, SH0 shape: {state.sh0.shape}")

# The SH0 values encode the adversarial color pattern
adversarial_colors = torch.sigmoid(state.sh0)  # [N, 3] RGB

Training

Hardware: NVIDIA L4 (23 GB VRAM)
Framework: PyTorch 2.11 + CUDA 12.8
Rasterizer: diff-gaussian-rasterization (CUDA kernel)
Optimizer: Adam (fused), lr=0.01, warmup + cosine decay
Batch size: 80 views per step (67.1% VRAM utilization)
Scene: 1M Gaussians
Resolution: 480×640
Steps: 20,000
Config: See configs/train_cuda.toml

Evaluation Targets (Paper §5)

Benchmark	Metric	Paper Value	Our Target
Digital white-box angle sweep	AP@0.5 on Faster R-CNN	6.73	≤ 8.0
Hardest angle (60°)	AP@0.5	0.00	≤ 2.0
1:1 real car	AP@0.5	25.67	≤ 30.0

ANIMA Module

Codename: DEF-RPGA
Domain: Defense
Wave: 7
API: FastAPI at port 8407
ROS2: Capture + bridge nodes for robotics integration
Docker: docker compose -f docker-compose.serve.yml --profile serve up -d

License

Apache 2.0 — Robot Flow Labs / AIFLOW LABS LIMITED

ilessio-aiflowlab/project_def_rpga

DEF-RPGA — Adversarial Camouflage via 3D Gaussian Splatting

Paper

Architecture