Table 19 Comparison of BER values between the proposed and existing methods13,43,44,47,48,45,46 under various attacks.

From: Robust zero-watermarking for color images using hybrid deep learning models and encryption

Attacks

BER

Suggested algorithm

13

43

44

47

48

45

46

Gaussian noise (0.1)

0

0.0002

0.0034

0.001

0.0010

0.01

0.0082

Gaussian noise (0.3)

0

0

0.0084

0.0026

0.0010

0.0106

0.0092

Gaussian noise (0.5)

0

0.0002

0.0093

0.0036

0.0010

0.0121

0.0102

Gaussian noise (0.01)

0

0

0.0323

0.0091

0.0296

0.0421

0.0172

Salt & Pepper noise (0.01)

0

0

0.0125

0.0091

0.0421

0.0926

0.0091

Salt & Pepper noise (0.03)

0.0005

0.0290

0.0124

0.0010

0.0542

0.116

JPEG compression

(F = 10)

0

0.0002

0.0253

0.0041

0

0.1231

0.4844

JPEG compression

(F = 30)

0

0

0.0135

0.0037

0.0983

0.4063

0.0131

JPEG compression

(F = 50)

0

0

0.0111

0.0021

0

0.0849

0.3438

0.0099

JPEG compression

(F = 70)

0

0

0.0039

0.0007

0.0005

0.0183

0.0713

0.0058

JPEG compression

(F = 90)

0

0

0.0018

0.0002

0.0002

0.0073

0.0213

Average filter (3 × 3)

0

0

0.0099

0.202

0.0001

0.0029

0.099

0.0079

Average filter (5 × 5)

0

0.0002

0.0181

0.0361

0.0001

0.298

0.0455

0.0157

Average filter (7 × 7)

0

0.0009

0.0245

0.0618

0.0001

0.2919

0.0630

Median filter (3 × 3)

0.0005

0.0002

0.0068

0.001

0

0.0039

0.0442

0.0064

Median filter (5 × 5)

0

0

0.0135

0.009

0.0007

0.02866

0.0505

0.0099

Median filter (7 × 7)

0

0.0002

0.0195

0.011

0.0001

0.02910

0.0984

Rotation (3°)

0.0005

0

0.00959

0.0002

0

0.1333

Rotation (5°)

0

0

0.02982

0.0002

0

0.1858

Scaling (0.5)

0

0

0.00659

0.0002

0

0.0071

Sharpening

0

0

0.0007

0

0.0001

0.00018

0.0009