New image steganographic methods using run-length approach Chin-Chen Chang a,b, * , Chih-Yang Lin b , Yu-Zheng Wang b a Department of Information Engineering and Computer Science, Feng Chia University, 100 Wenhwa Road, Seatwen, Taichung 40724, Taiwan, ROC b Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi 621, Taiwan, ROC Received 29 December 2004; received in revised form 24 January 2006; accepted 3 February 2006 Abstract This study proposes two efficient data hiding methods incorporating both run-length encoding and modular arithmetic. The first method, BRL (hiding bitmap files by run- length), is suitable for embedding simple data with long streams of repeating bits; the second method, GRL (hiding general files by run-length), is good for embedding com- plicated data with short streams of repeating bits. Both of the new methods embed secret data in each nonoverlapping block composed of only two consecutive pixels, with the image quality controlled by a modular operation. In addition, the concept of run- length encoding – namely recording the number of repeating bits in the secret data and the bit value itself – is applied for simplicity and efficiency. The experimental results demonstrate that BRL and GRL have their own strengths with respect to different types of secret data. Furthermore, on the average, both can outperform other well-accepted image steganographic methods in terms of image quality and embedding capacity. Ó 2006 Elsevier Inc. All rights reserved. 0020-0255/$ - see front matter Ó 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.ins.2006.02.008 * Corresponding author. Address: Department of Information Engineering and Computer Science, Feng Chia University, 100 Wenhwa Road, Seatwen, Taichung 40724, Taiwan, ROC. Tel.: +886 4 24517250x3790; fax: +886 4 27066495. E-mail addresses: ccc@cs.ccu.edu.tw (C.-C. Chang), gary@cs.ccu.edu.tw (C.-Y. Lin), wyc92@cs.ccu.edu.tw (Y.-Z. Wang). Information Sciences 176 (2006) 3393–3408 www.elsevier.com/locate/ins