Adversarial Batch Image Steganography against CNN-based Pooled Steganalysis

Abstract The application of adversarial embedding in single image steganography exhibits its advantage in resisting convolutional neural network (CNN)-based steganalysis. As an important technique to move the steganography from the laboratory to the real world, batch steganography is developed based on the single image steganography, which uses a series of images as carriers. Furthermore, existing pooled steganalysis also applied CNN architecture for feature extraction, which aims to detect batch steganography. Therefore, it is reasonable and meaningful to introduce adversarial embedding in batch steganography to resist pooled steganalysis. However, as far as we know, there is no work about adversarial batch steganography. Adversarial batch image steganography should be able to resist pooled steganalysis which takes a group of images as a unit, therefore the loss function of the single image steganalyzer can not be directly used for adversarial embedding. In addition, adversarial embedding should be combined with batch strategy. In this paper, we propose a general framework of adversarial embedding for batch steganography, in which a new loss function is designed and the batch strategy is combined with adversarial embedding. By this framework, we can adapt most adversarial embedding algorithms for single image steganography to batch steganography. To verify the efficiency of the proposed framework, we design an algorithm called ADVersarial Image Merging Steganography (ADV-IMS) based on ADVersarial EMBedding (ADV-EMB), and carry out a series corresponding experiments. Experimental results show the proposed method significantly improves the security performance of batch steganography against pooled steganalysis and keeps a high-security level against single image steganalysis.