Quick and reliable algorithm to determine the existence of a QR code in an image?

Without implementing openCV or calling QR code's recognition API, is there any quick and reliable algorithm to determine the existence of a QR code in an image?

The intention of this question is to improve the user experience of scanning QR code. When QR code's recognition fails, the program needs to know whether there really exists a QR code for it to scan and recognize QR code again or there is not any QR code so that the program can call other procedures.

To echo some response, the detection program doesn't need to be 100% accurate but returns an accurate result with reasonable probability. If we can use openCV here, Fourier Transformation will be easily implemented to detect whether there is an obvious high frequency in an image, which is a good sign of the existence of QR. But the integration of openCV will largely increase the size of my program, which I want to avoid.

1. Convert the image into grayscale
2. Divide the image into cells of n x m, say 3 x 3. This procedure intends to guarantee that at the least one cell will be fully covered by possible QR code if any
3. Implement 2D Fourier Transformation for all the cells. If in any cell there is an significantly large value in high-frequency area in both X and Y axis, there is a high likelihood that there exists a QR code

I am addressing a probability issue rather than 100% accurate detection. In this algorithm, chessboard will be detected as QR code as well.