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CNN-based Adversarial Embedding for Image Steganography

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Abstract:

Steganographic schemes are commonly 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 (ADV-EMB), 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. In particular, ADV-EMB adjusts the costs of image elements modifications according to the gradients back propagated from the target CNN steganalyzer. Therefore, modification direction has a higher probability to be the same as the inverse sign of the gradient. In this way, the so called adversarial stego images are generated. Experiments demonstrate that the proposed steganographic scheme achieves better security performance against the target adversary-unaware steganalyzer by increasing its missed detection rate. 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.

 

Introduction:

Image steganography is the art and science of concealing covert information within images. It is usually achieved by modifying image elements, such as pixels or DCT coefficients. On the other side of the game, steganalysis aims to reveal the presence of secret information by detecting whether there are abnormal artefacts left by data embedding. The developing history of steganography and steganalysis is rich of interesting stories, as they compete with each other and The embedding cost of changing each image element is specified by a cost function, and a coding scheme is employed to convey information by minimizing the distortion,

 

 Existing Methods:

  • Discrete Cosine transformation
  • Direct bit replacement Process

 

 Drawbacks:

  • Embedding Robustness is less
  • Edge information loss due to ringing artifact.

 

 Proposed System:

  • CNN
  • ADV-EMB (Adversarial Embedding). Targeted to counter a deep learning

 

 Advantages:

  • High hiding Capacity
  • Less degradation in Image quality during hiding

 

Block Diagram:

CNN-based Adversarial Embedding for Image Steganography

 

Application:

  • Research institute.
  • Medical information protection.
  • Defense application.
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  • Price
    Free
  • Instructor pantech team
  • Duration 15 Hrs
  • Enrolled 0 student
  • Access 3 Months

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