Reversible data hiding in encrypted images using adaptive block-level prediction-error expansion Source: Signal Processing: Image Communication 64 (2018) 78-88 Authors: Shuang Yi, Yicong Zhou, Zhongyun Hua Speaker: Qun-Feng Zeng Date: 17/5/2018 1 1

Outline Related Works Proposed Scheme Experiment Results Conclusions 2

Related Works- Block-level prediction error expansion 100 101 99 101 102 99 101 102 99 101 102 99 𝑠=0 𝑠=1 𝑝′ 1,1 =100 𝑒=0 𝑝" 1,1 =101 𝑝′ 1,2 =100 𝑒=1 𝑝" 1,2 =102 𝑝′ 2,1 =100 𝑒=−1 𝑝" 2,1 =99 𝑝′ 2,2 =100 𝑒=1 𝑝" 2,2 =102 𝑝′ 1,1 = 𝑝 1,2 ×0.4+ 𝑝 2,1 ×0.4+ 𝑝 2,2 ×0.2 =100 𝑝′ 2,1 = 𝑝 1,1 ×0.4+ 𝑝 2,2 ×0.4+ 𝑝 1,2 ×0.2 =100 𝑝′ 2,2 = 𝑝 1,2 ×0.4+ 𝑝 2,1 ×0.4+ 𝑝 1,1 ×0.2 =100 𝑝′ 1,2 = 𝑝 1,1 ×0.4+ 𝑝 2,2 ×0.4+ 𝑝 2,1 ×0.2 =100 𝑒= 𝑝 1,1 − 𝑝 ′ 1,1 =100−100=0 𝑒= 𝑝 2,2 − 𝑝 ′ 2,2 =101−100=1 𝑒= 𝑝 2,1 − 𝑝 ′ 2,1 =99−100=−1 𝑒= 𝑝 1,2 − 𝑝 ′ 1,2 =101−100=1 𝑠=1 𝑝" 2,2 =101+1=102 𝑠=0 𝑝" 1,2 =101+1=102 𝑝" 1,1 =100+1=101 𝑝" 2,1 =99+0=99

Proposed Scheme- Adaptive block-level prediction error expansion 𝑟𝑜𝑢𝑛𝑑 𝑎𝑛𝑑 𝑙𝑎𝑦𝑒𝑟𝑠 𝑠𝑖𝑧𝑒 𝑜𝑓 𝑝𝑎𝑦𝑙𝑜𝑎𝑑 𝑜𝑣𝑒𝑟𝑓𝑙𝑜𝑤

Proposed Scheme- Adaptive block-level prediction error expansion Complex for sub image B 49 48 54 53 56 57 85 45 46 60 63 49 48 61 54 53 62 64 𝑎𝑣𝑔= 49+48+54+53 4 =51 𝑒 1 =49−51=−2 𝑒 2 =48−51=−3 𝑒 3 =54−51=3 𝑒 4 =53−51=2 57 61 62 64 𝑎𝑣𝑔= 57+61+62+64 4 =61 𝐶 𝐵 = 𝑒=−3 2 ℎ(𝑒) 4𝑆 = 6 8 =0.75 𝑒 1 =57−61=−4 𝑒 2 =61−61=0 𝑒 3 =62−61=1 𝑒 4 =64−61=3

Proposed Scheme- Adaptive block-level prediction error expansion 𝐶 𝐵 =0.75, 𝑟=0.3 𝑇=9

Proposed Scheme- Adaptive block-level prediction error expansion Complex for each pixel 𝐶 𝑏 = max 𝑥 𝑟 , 𝑥 𝑐 , 𝑥 𝑑 −m𝑖𝑛⁡{ 𝑥 𝑟 , 𝑥 𝑐 , 𝑥 𝑑 } 49 48 54 53 𝐶 1,1 = max 𝑝 1,2 , 𝑝 2,1 , 𝑝 2,2 −𝑚𝑖𝑛 𝑝 1,2 , 𝑝 2,1 , 𝑝 2,2 = max 48,54,53 −𝑚𝑖𝑛 48,54,53 =54−48=6 𝐶 1,2 =54−49=5 𝐶 2,1 =53−48=5 𝐶 2,2 =54−48=6 57 61 62 64 𝐶 1,1 =64−61=3 𝐶 1,2 =64−57=7 𝐶 2,1 =64−57=7 𝐶 2,2 =62−57=5

Experiment Results PSNR (dB) Embedding rate (bpp) 0.005 0.02 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.5 BPEE 62.18 57.87 54.32 51.49 49.75 48.13 45.81 44.32 43.12 41.74 39.65 Lena MED 64.23 58.52 55.50 52.19 49.70 46.81 45.00 43.28 41.82 40.27 37.30 ABPEE 64.89 58.78 55.93 52.71 50.31 48.45 46.27 44.89 43.63 42.30 39.86 64.34 60.11 56.58 53.67 51.90 50.59 49.60 48.60 46.70 45.32 43.03 Airplane 67.25 63.00 58.55 54.54 52.79 50.03 48.43 47.14 43.50 41.13 67.81 63.58 59.27 55.87 53.78 52.43 51.15 49.29 47.61 46.38 43.72 [35] M.J. Weinberger, G. Seroussi, G. Sapiro, The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS, IEEE Trans. Image Process. 9 (8) (2000) 1309–1324.(MED:median edge dector) 8 8

Experiment Results 9 9 PSNR (dB) Embedding rate (bpp) 0.005 0.02 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.5 BPEE 60.76 56.39 52.84 49.99 47.71 44.84 43.07 41.19 39.79 38.33 35.93 Barbara MED 63.64 59.28 55.34 51.09 47.42 45.02 42.89 40.69 38.98 37.07 33.88 ABPEE 63.93 59.32 55.60 52.10 48.87 46.32 44.43 42.72 41.10 39.75 36.46 62.39 58.06 54.57 51.58 49.84 48.32 45.85 44.27 42.98 41.53 39.27 Boat 64.40 60.14 55.67 52.56 50.13 47.04 45.40 43.38 41.93 40.24 37.21 65.05 60.70 56.46 53.26 50.82 49.04 46.79 45.21 43.80 42.50 39.56 9 9

Experiment Results 10 10 PSNR (dB) Embedding rate (bpp) 0.005 0.02 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.5 BPEE 57.19 52.99 49.38 44.10 40.86 38.35 35.90 34.14 32.50 31.01 Baboon MED 60.03 54.85 49.77 44.11 40.18 37.03 34.30 32.32 30.42 ABPEE 59.87 55.02 50.55 45.23 41.62 38.75 36.10 34.31 32.64 31.00 61.56 57.29 53.70 50.82 49.04 Peppers 62.32 57.97 49.54 46.80 44.30 42.17 40.19 62.28 58.57 54.10 51.06 48.57 46.26 44.64 42.61 41.26 40.10 10 10

Experiment Results 11 11

Experiment Results 12 12

Conclusions ABPEE Less payload Good PSNR 14 14