Abstract:This paper proposes an image authentication scheme for mobile devices. The proposed scheme generates an image watermark by using discrete cosine transform (DCT) and hides the watermark in the spatial pixels for image authentication and tamper detection. The hiding operator used in this paper is very simple in a mobile environment allowing high-speed authentication using a low-power mobile device. The quality of the stego-image and the recovered image becomes excellent as a result of the proposed scheme.
“…The conditions that must be met by an original image in the watermark embedding process using Discrete Cosine Transform (DCT) are stated in Equation (1).…”
Section: Figure 1 the Proposed Watermark Embeddingmentioning
“…Many techniques have been proposed to protect images, such as cryptography, watermarking, digital signatures, and so on [1]. In this study, the proposed technique is watermarking.…”
Uploading an identity card as an image for the account verification process or transactions online can be a threat to application users. Identity card theft can be carried out by irresponsible persons if the application can be hacked. Therefore, protection of the image is required for authentication. In this study, the proposed technique is watermarking. A watermark in the form of a binary image will be embedded into the image as ownership using a Discrete Cosine Transform. The Discrete Cosine Transform works in the frequency domain. The location of the embedding of different watermarks was analysed in each 8×8 DCT block. The results of the analysis to assess the imperceptibility of original images and watermarked images using PSNR (Peak Signal to Noise Ratio) and SSIM (Structural Similarity Index Measure), while assessing the watermark robustness embedded using NCC (Normalized Cross Correlation). The results show PSNR (Peak Signal to Noise Ratio) ≥ 54 dB with a watermark strength of 0,1 and an average SSIM (Structural Similarity Index Measure) ≥ 0,9 on 4 scanned images in BMP format with a resolution of 100 DPI. A good watermark embedding is done on the green component at middle frequencies to maintain a balance between imperceptibility and robustness. In contrast, the red component at low frequency is vulnerable to attacks in the form of brightness +20 and contrast +50 with an average NCC (Normalized Cross Correlation) ≤ 0,85.
“…The conditions that must be met by an original image in the watermark embedding process using Discrete Cosine Transform (DCT) are stated in Equation (1).…”
Section: Figure 1 the Proposed Watermark Embeddingmentioning
“…Many techniques have been proposed to protect images, such as cryptography, watermarking, digital signatures, and so on [1]. In this study, the proposed technique is watermarking.…”
Uploading an identity card as an image for the account verification process or transactions online can be a threat to application users. Identity card theft can be carried out by irresponsible persons if the application can be hacked. Therefore, protection of the image is required for authentication. In this study, the proposed technique is watermarking. A watermark in the form of a binary image will be embedded into the image as ownership using a Discrete Cosine Transform. The Discrete Cosine Transform works in the frequency domain. The location of the embedding of different watermarks was analysed in each 8×8 DCT block. The results of the analysis to assess the imperceptibility of original images and watermarked images using PSNR (Peak Signal to Noise Ratio) and SSIM (Structural Similarity Index Measure), while assessing the watermark robustness embedded using NCC (Normalized Cross Correlation). The results show PSNR (Peak Signal to Noise Ratio) ≥ 54 dB with a watermark strength of 0,1 and an average SSIM (Structural Similarity Index Measure) ≥ 0,9 on 4 scanned images in BMP format with a resolution of 100 DPI. A good watermark embedding is done on the green component at middle frequencies to maintain a balance between imperceptibility and robustness. In contrast, the red component at low frequency is vulnerable to attacks in the form of brightness +20 and contrast +50 with an average NCC (Normalized Cross Correlation) ≤ 0,85.
“…However, some of them could not withstand a vector quantization attack [9] or the tampering coincidence problem [10]. To overcome those problems, other fragile watermarking strategy-based block mappings were proposed by [11][12][13][14][15][16]. In these schemes, the original image is divided into non-overlapping blocks and the authentication code is generated by employing different kinds of technologies for each block, including the discrete cosine transform (DCT)-based method [11,12], the singular value decomposition (SVD)-based method [13,14], and the coding-based method [15,16].…”
Section: Introductionmentioning
confidence: 99%
“…Verification is conducted by comparing the extracted and recalculated authentication code. These schemes [11][12][13][14][15] also adopt a multi-hierarchical tampering detection strategy to improve the tampering detection rate; for example, a first hierarchical tampering detection strategy is used to initially identify the tampered area and a second hierarchical tampering detection strategy serves as a remediation measure. As a consequence, these approaches have high precision in tampering detection.…”
In this paper, a high-precision image authentication scheme for absolute moment block truncation coding (AMBTC)-compressed images is presented. For each block, two sub-bitmaps are conducted using the symmetrical separation, and the six-bit authentication code is symmetrically assigned to two sub-codes, which is virtually embedded into sub-bitmaps using the matrix encoding later. To overcome distortion caused by modifications to the bitmap, the corresponding to-be-flipped bit-location information is recorded instead of flipping these bits of the bitmap directly. Then, the bit-location information is inserted into quantization levels based on adjusted quantization level matching. In contrast to previous studies, the proposed scheme offers a significantly improved tampering detection ability, especially in the first hierarchical tampering detection without remediation measures, with an average tampering detection rate of up to 98.55%. Experimental results show that our approach provides a more stable and reliable tampering detection performance and sustains an acceptable visual quality.
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