CNN Based Adversarial Embedding with Minimum Alteration for Image Steganography

by   Weixuan Tang, et al.

Historically, steganographic schemes were designed in a way to preserve image statistics or steganalytic features. Since most of the state-of-the-art steganalytic methods employ a machine learning (ML) based classifier, it is reasonable to consider countering steganalysis by trying to fool the ML classifiers. However, simply applying perturbations on stego images as adversarial examples may lead to the failure of data extraction and introduce unexpected artefacts detectable by other classifiers. In this paper, we present a steganographic scheme with a novel operation called adversarial embedding, which achieves the goal of hiding a stego message while at the same time fooling a convolutional neural network (CNN) based steganalyzer. The proposed method works under the conventional framework of distortion minimization. Adversarial embedding is achieved by adjusting the costs of image element modifications according to the gradients backpropagated from the CNN classifier targeted by the attack. Therefore, modification direction has a higher probability to be the same as the sign of the gradient. In this way, the so called adversarial stego images are generated. Experiments demonstrate that the proposed steganographic scheme is secure against the targeted adversary-unaware steganalyzer. In addition, it deteriorates the performance of other adversary-aware steganalyzers opening the way to a new class of modern steganographic schemes capable to overcome powerful CNN-based steganalysis.


page 1

page 2

page 3

page 4


Image Steganography based on Iteratively Adversarial Samples of A Synchronized-directions Sub-image

Nowadays a steganography has to face challenges of both feature based st...

Show-and-Fool: Crafting Adversarial Examples for Neural Image Captioning

Modern neural image captioning systems typically adopt the encoder-decod...

Enhancing JPEG Steganography using Iterative Adversarial Examples

Convolutional Neural Networks (CNN) based methods have significantly imp...

CNN-based Steganalysis and Parametric Adversarial Embedding: a Game-Theoretic Framework

CNN-based steganalysis has recently achieved very good performance in de...

Are Adversarial Perturbations a Showstopper for ML-Based CAD? A Case Study on CNN-Based Lithographic Hotspot Detection

There is substantial interest in the use of machine learning (ML) based ...

Adversarial Machine Learning-Based Anticipation of Threats Against Vehicle-to-Microgrid Services

In this paper, we study the expanding attack surface of Adversarial Mach...

Adversarial Imaging Pipelines

Adversarial attacks play an essential role in understanding deep neural ...

Please sign up or login with your details

Forgot password? Click here to reset