Hi.
I would go for Binary cross entroype loss instead.
Besides, which kind of modifications are done in the images to be considered as “modified”.
If the examples are very hard the network may not learn. You can try to go from simple examples to more challenging ones. The accuracy you get basically means the results are random, thus, the network is doing nothing. In addition, you may want to increse the amount of parameters. This depends on what kind of images you have.