Nvidia Digits批量大小、数据混洗机制及隐写图像检测训练的技术疑问

1. Is pairing cover/stego images the right approach? #

Absolutely—this is a smart, targeted strategy for steganography detection, and it aligns with how Siamese Networks (or contrastive learning frameworks) work. Here's why it makes sense:

• Steganographic changes are often extremely subtle (think tiny pixel adjustments to hide data). Pairing a cover image with its corresponding stego version lets your network focus directly on the differences between them, rather than learning generic image features that might not capture those tiny tweaks.
• That said, a standard binary classification approach (feeding single images and training the network to label them as "cover" or "stego") is also valid. But the paired approach tends to perform better for steg detection because it eliminates noise from unrelated image variations (like different scenes) and zeroes in on the specific artifacts introduced by steganography tools.

2. How does batch size work, and how to get one cover + one stego per batch? #

Let’s break this down based on how you’re loading data:

• Default behavior (e.g., ImageDataGenerator or standard tf.data.Dataset): Batch size refers to the total number of individual samples loaded per batch. If you set batch_size=1, it will pick one random sample from the entire combined dataset (covers + stegos)—not one from each directory. Setting batch_size=2 also doesn’t guarantee one from each class; it could be two covers, two stegos, or one of each, depending on how your data is sampled.
• To get exactly one cover + one stego per batch: You’ll need to create a custom data pipeline. Here’s a quick outline:
  1. Create separate datasets for covers and stegos.
  2. Zip them together so each element is a tuple (cover_img, stego_img).
  3. Set your batch size to the number of such pairs you want per batch (e.g., batch_size=1 gives you one pair: 1 cover + 1 stego).
     If you’re using DIGITS, you might need to structure your dataset to explicitly group paired images (e.g., naming them cover_001.jpg and stego_001.jpg and writing a custom loader to pair them).

3. How does epoch shuffling work, and do paired images stay aligned? #

By default, most TensorFlow/DIGITS data loaders shuffle the entire list of samples as a single pool. This means:

• Your cover and stego directories are merged into one list of samples, and shuffling randomizes the order of this entire list. There’s no guarantee that cover/1.jpg and stego/1.jpg will be near each other or paired after shuffling.
• If you need to keep paired images aligned (so the network always sees the exact cover-stego pair together), you can’t rely on default shuffling. Instead:
  1. Create a list of paired sample paths (e.g., [(cover_path_1, stego_path_1), (cover_path_2, stego_path_2), ...]).
  2. Shuffle this list of pairs as whole units before each epoch. This way, the pairs stay intact, but their order in the dataset is randomized to prevent overfitting.

内容的提问来源于stack exchange，提问作者Shenath Silva