This is known as one-class learning or one-class classification. You could try obtaining "background class" images (images similar to yours in resolution, overall brightness, ...) and training an ordinary classifier on the combination of the two. Obviously, the background class images cannot contain food, but searches for things unrelated to food ("nail", "dancer", "floor", "statue", ...) followed up by quick visual inspection should serve.