Image Tamper Detection Using Quantization Table Estimation - Hamdy, Salma
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Most image acquisition and editing tools use the JPEG standard for image compression. Quantization table estimation is essential for establishing bitmap compression history, which is particularly useful in applications like image authentication, JPEG artifact removal, and JPEG re-compression with less distortion. The histogram of Discrete Cosine Transform DCT coefficients contains information on the compression parameters for single JPEG compressed and previously compressed bitmaps. One method proposed here is based on inspecting the peaks of the histogram of DCT to estimate quantization…mehr

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Produktbeschreibung
Most image acquisition and editing tools use the JPEG standard for image compression. Quantization table estimation is essential for establishing bitmap compression history, which is particularly useful in applications like image authentication, JPEG artifact removal, and JPEG re-compression with less distortion. The histogram of Discrete Cosine Transform DCT coefficients contains information on the compression parameters for single JPEG compressed and previously compressed bitmaps. One method proposed here is based on inspecting the peaks of the histogram of DCT to estimate quantization steps. Another, based on streamed DCT coefficients, reconstructs dequantized DCT coefficients which are then used with their corresponding compressed values to estimate quantization steps. Extending the two methods to bitmaps proves very helpful in identifying previous compression, and quantization tables if any. The estimated table is used with two distortion measures; blocking artifact, and average distortion, for inspecting possible local forgeries. The methods score poorly or fail with heavy or double compression.
Autorenporträt
Dr. Salma Hamdy is an assistant professor in computer science department, FCIS, Ain Shams University, teaching image processing and basic programming.She did masters in digital watermarking and Phd in passive image forensics. Her research interests also include scene understanding, HCI, and medical image processing.