• Title/Summary/Keyword: 공격 검출 알고리즘

Search Result 72, Processing Time 0.015 seconds

Threshold Selection Method for Capacity Optimization of the Digital Watermark Insertion (디지털 워터마크의 삽입용량 최적화를 위한 임계값 선택방법)

  • Lee, Kang-Seung;Park, Ki-Bum
    • Journal of the Institute of Convergence Signal Processing
    • /
    • v.10 no.1
    • /
    • pp.49-59
    • /
    • 2009
  • In this paper a watermarking algorithm is proposed to optimize the capacity of the digital watermark insertion in an experimental threshold using the characteristics of human visual system(HVS), adaptive scale factors, and weight functions based on discrete wavelet transform. After the original image is decomposed by a 3-level discrete wavelet transform, the watermarks for capacity optimization are inserted into all subbands except the baseband, by applying the important coefficients from the experimental threshold in the wavelet region. The adaptive scale factors and weight functions based on HVS are considered for the capacity optimization of the digital watermark insertion in order to enhance the robustness and invisibility. The watermarks are consisted of gaussian random sequences and detected by correlation. The experimental results showed that this algorithm can preserve a fine image quality against various attacks such as the JPEG lossy compression, noise addition, cropping, blurring, sharpening, linear and non-linear filtering, etc.

  • PDF

Digital Watermarking using ART2 Algorithm (ART2 알고리즘을 이용한 디지털 워터마킹)

  • 김철기;김광백
    • Journal of Intelligence and Information Systems
    • /
    • v.9 no.3
    • /
    • pp.81-97
    • /
    • 2003
  • In this paper, we suggest a method of robust watermarking for protection of multimedia data using the wavelet transform and artificial neural network. for the purpose of implementation, we decompose a original image using wavelet transform at level 3. After we classify transformed coefficients of other subbands using neural network except fur the lowest subband LL$_3$, we apply a calculated threshold about chosen cluster as the biggest. We used binary logo watermarks to make sure that it is true or not on behalf of the Gaussian Random Vector. Besides, we tested a method of dual watermark insertion and extraction. For the purpose of implementation, we decompose a original image using wavelet transform at level 3. After we classify transformed coefficients of other subbands using neural network except for the lowest subband LL$_3$, we apply a above mentioned watermark insert method. In the experimental results, we found that it has a good quality and robust about many attacks.

  • PDF