We introduce GreenCOD, a green method for detecting camouflaged objects distinct in its avoidance of backpropagation techniques. GreenCOD leverages gradient boosting and deep features extracted from pre-trained Deep Neural Networks. Traditional camouflaged object detection approaches rely on complex deep neural networks, seeking performance improvements by backpropagation-based finetuning. However, such methods are typically computationally demanding and exhibit only marginal performance variations across different models. It raises the question of whether effective training can be achieved without backpropagation. In this direction, our work proposes a new paradigm that utilizes gradient boosting for COD. This approach significantly simplifies the model design, resulting in a system that requires fewer parameters and operations and maintains high performance compared to state-of-the-art deep learning models. Remarkably, our models are trained without backpropagation and achieve the best performance with fewer than 20G Multiply-Accumulate Operations. This new, more efficient paradigm opens avenues for further exploration in green, backpropagation-free model training. We make GreenCOD source code and on-device demo available at https://greencod.ai/ for futher research.