방송공학회논문지 (Journal of Broadcast Engineering)

  • 제24권2호
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  • Pages.315-328
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  • 2019
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  • 1226-7953(pISSN)
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  • 2287-9137(eISSN)
한국방송∙미디어공학회 (The Korean Institute of Broadcast and Media Engineers)
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구조적인 차이를 가지는 CNN 기반의 스테그아날리시스 방법의 실험적 비교

Experimental Comparison of CNN-based Steganalysis Methods with Structural Differences

  • 투고 : 2018.12.31
  • 심사 : 2019.02.18
  • 발행 : 2019.03.30
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⟨ 이전 논문 다음 논문 ⟩

초록

영상 스테그아날리시스는 입력 영상을 스테가노그래피 알고리즘이 적용된 스테고 영상과 스테가노그래피 알고리즘이 적용되지 않은 커버 영상으로 분류하는 알고리즘이다. 기존에는 주로 수제 특징 기반의 스테그아날리시스를 연구하였다. 하지만 CNN 기반의 물체 인식이 큰 성과를 이루면서 최근 CNN 기반의 스테그아날리시스가 활발히 연구되고 있다. CNN 기반의 스테그아날리시스는 물체 인식과는 달리 커버 영상과 스테고 영상의 미세한 차이를 식별하기 위해서 전처리 필터를 필요로 한다. 그러므로, CNN 기반의 스테그아날리시스 연구들은 효과적인 전처리 필터와 네트워크 구조를 개발하는 데 초점을 두고 있다. 본 논문에서는 동일한 실험 조건에서 기존 연구들을 비교하고, 그 결과를 기반으로 전처리 필터와 네트워크 구조적인 차이에 의한 성능 변화를 분석한다.

Image steganalysis is an algorithm that classifies input images into stego images with steganography methods and cover images without steganography methods. Previously, handcrafted feature-based steganalysis methods have been mainly studied. However, CNN-based objects recognition has achieved great successes and CNN-based steganalysis is actively studied recently. Unlike object recognition, CNN-based steganalysis requires preprocessing filters to discriminate the subtle difference between cover images from stego images. Therefore, CNN-based steganalysis studies have focused on developing effective preprocessing filters as well as network structures. In this paper, we compare previous studies in same experimental conditions, and based on the results, we analy ze the performance variation caused by the differences in preprocessing filter and network structure.

키워드

BSGHC3_2019_v24n2_315_f0001.png 이미지

그림 1. BOSSBase 데이터 셋의 1,000장의 커버 영상과 1LSB(bpp = 0.4), S-UNIWARD(bpp = 0.4)에 의한 스테고 영상의 이웃 픽셀 사이의 값 차이 분포 Fig. 1. Histogram of neighboring pixel value differences of 1,000 cover images in BOSSBase data sets and their stego images made by 1LSB(bpp = 0.4) and S-UNIWARD(bpp = 0.4)

BSGHC3_2019_v24n2_315_f0005.png 이미지

그림 5. ABS와 BN 적용 전/후 히스토그램. (a) 입력, (b) ABS 적용 후, BN 적용 후 Fig. 5. Histogram before/after applying ABS and BN. (a) input, (b) after ABS, (c) after BN

BSGHC3_2019_v24n2_315_f0006.png 이미지

그림 6. WOW와 S-UNIWARD에 사용된 16 × 16 크기의 3가지 필터 Fig. 6. Three 16 × 16 filters used in WOW and S-UNIWARD

BSGHC3_2019_v24n2_315_f0007.png 이미지

그림 7. S-UNIWARD와 WOW에 의한 비밀 정보 삽입 영역. (a) 커버 영상, (b) S-UNIWARD, (c) WOW Fig. 7. Secret information insertion regions by S-UNIWARD and WOW. (a) Cover image, (b) S-UNIWARD, (c) WOW

BSGHC3_2019_v24n2_315_f0009.png 이미지

그림 9. DAM 구조 Fig. 9. DAM structure

BSGHC3_2019_v24n2_315_f0010.png 이미지

그림 10. 성능 분석에 사용된 CNN 기반의 스테그아날리시스 방법의 네트워크 구조. (a) [2]의 방법, (b) [3]의 방법, (c) [4]의 방법, (d) [5]의 방법, (e) [6]의 방법 Fig. 10. Network structures of CNN-based steganalysis methods used in performance analysis. (a) Method of [2], (b) method of [3], (c) method of [4], (d) method of [5], (e) method of [6]

BSGHC3_2019_v24n2_315_f0011.png 이미지

그림 2. 1차원 입력을 가지는 신경망의 계층. (a) 완전 연결 계층, (b) 합성곱 계층 Fig. 2. Layers in 1D-input neural network. (a) Fully connected layer, (b) convolutional layer

BSGHC3_2019_v24n2_315_f0012.png 이미지

그림 3. CNN 파라미터. 서로 다른 패딩 크기, 필터의 크기, 스트라이드, 필터 채널을 가지는 두 CNN, 각각 A = {0, 3, 1, 2}, B = {2, 5, 2, 1} Fig. 3. CNN parameters, Two CNNs with different padding sizes, filter sizes, stride, and filter channels, A = {0, 3, 1, 2}, B = {2, 5, 2, 1}

BSGHC3_2019_v24n2_315_f0013.png 이미지

그림 4. Xu-Net[2]에서 제안한 CNN Fig. 4. CNN proposed in Xu-net[2]

BSGHC3_2019_v24n2_315_f0014.png 이미지

그림 8. 4가지 활성화 함수. (a) TLU, (b) ReLU, (c) tanH, (d) sigmoid Fig. 8. 4 activation functions. (a) TLU, (b) ReLU, (c) tanH, (d) sigmoid

표 1. S-UNIWARD 알고리즘 분류 결과 Table 1. Classification results for S-UNIWARD

BSGHC3_2019_v24n2_315_t0001.png 이미지

표 2. WOW 알고리즘 분류 결과 Table 2. Classification results for WOW

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표 3. 실험에 사용된 각 CNN의 하이퍼파라미터 Table 3. Hyperparameters of CNNs used in experiments

BSGHC3_2019_v24n2_315_t0003.png 이미지

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