884 research outputs found
Using Facebook for Image Steganography
Because Facebook is available on hundreds of millions of desktop and mobile
computing platforms around the world and because it is available on many
different kinds of platforms (from desktops and laptops running Windows, Unix,
or OS X to hand held devices running iOS, Android, or Windows Phone), it would
seem to be the perfect place to conduct steganography. On Facebook, information
hidden in image files will be further obscured within the millions of pictures
and other images posted and transmitted daily. Facebook is known to alter and
compress uploaded images so they use minimum space and bandwidth when displayed
on Facebook pages. The compression process generally disrupts attempts to use
Facebook for image steganography. This paper explores a method to minimize the
disruption so JPEG images can be used as steganography carriers on Facebook.Comment: 6 pages, 4 figures, 2 tables. Accepted to Fourth International
Workshop on Cyber Crime (IWCC 2015), co-located with 10th International
Conference on Availability, Reliability and Security (ARES 2015), Toulouse,
France, 24-28 August 201
A novel steganography approach for audio files
We present a novel robust and secure steganography technique to hide images into audio files aiming at increasing the carrier medium capacity. The audio files are in the standard WAV format, which is based on the LSB algorithm while images are compressed by the GMPR technique which is based on the Discrete Cosine Transform (DCT) and high frequency minimization encoding algorithm. The method involves compression-encryption of an image file by the GMPR technique followed by hiding it into audio data by appropriate bit substitution. The maximum number of bits without significant effect on audio signal for LSB audio steganography is 6 LSBs. The encrypted image bits are hidden into variable and multiple LSB layers in the proposed method. Experimental results from observed listening tests show that there is no significant difference between the stego audio reconstructed from the novel technique and the original signal. A performance evaluation has been carried out according to quality measurement criteria of Signal-to-Noise Ratio (SNR) and Peak Signal-to-Noise Ratio (PSNR)
Work design improvement at Miroad Rubber Industries Sdn. Bhd.
Erul Food Industries known as Salaiport Industry is a family-owned company and was established on July 2017. Salaiport Industry apparently moved to a new place at Pedas, Negeri Sembilan. Previously, Salaiport Industry operated in-house located at Pagoh, Johor. This small company major business is producing frozen smoked beef, smoked quail, smoke catfish and smoked duck. The main frozen product is smoked beef. The frozen smoked meat produced by Salaiport Industry is depending on customer demands. Usually the company produce 40 kg to 60 kg a day and operated between for four days until five days. Therefore, the company produce approximately around 80 kg to 120 kg per week. The company usually take 2 days for 1 complete cycle for the production as the first day the company will only receive the meat from the supplier and freeze the meat for use of tomorrow
JPEG steganography: A performance evaluation of quantization tables
The two most important aspects of any image based steganographic system are the imperceptibility and the capacity of the stego image. This paper evaluates the performance and efficiency of using optimized quantization tables instead of default JPEG tables within JPEG steganography. We found that using optimized tables significantly improves the quality of stego-images. Moreover, we used this optimization strategy to generate a 16x16 quantization table to be used instead of that suggested. The quality of stego-images was greatly improved when these optimized tables were used. This led us to suggest a new hybrid steganographic method in order to increase the embedding capacity. This new method is based on both and Jpeg-Jsteg methods. In this method, for each 16x16 quantized DCT block, the least two significant bits (2-LSBs) of each middle frequency coefficient are modified to embed two secret bits. Additionally, the Jpeg-Jsteg embedding technique is used for the low frequency DCT coefficients without modifying the DC coefficient. Our experimental results show that the proposed approach can provide a higher information-hiding capacity than the other methods tested. Furthermore, the quality of the produced stego-images is better than that of other methods which use the default tables
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High capacity steganographic method based upon JPEG
The two most important aspects of any image-based
steganographic system are the quality of the stegoimage and the capacity of the cover image. This paper proposes a novel and high capacity steganographic approach based on Discrete Cosine Transformation (DCT) and JPEG compression. JPEG technique divides the input image into non-overlapping blocks of 8x8 pixels and uses the DCT transformation. However, our proposed method divides the cover image into nonoverlapping
blocks of 16x16 pixels. For each quantized
DCT block, the least two-significant bits (2-LSBs) of each middle frequency coefficient are modified to embed two secret bits. Our aim is to investigate the data hiding efficiency using larger blocks for JPEG compression. Our experiment result shows that the proposed approach can provide a higher information hiding capacity than Jpeg-Jsteg and Chang et al. methods based on the conventional blocks of 8x8 pixels. Furthermore, the produced stego-images are almost identical to the original cover images
Deep Convolutional Neural Network to Detect J-UNIWARD
This paper presents an empirical study on applying convolutional neural
networks (CNNs) to detecting J-UNIWARD, one of the most secure JPEG
steganographic method. Experiments guiding the architectural design of the CNNs
have been conducted on the JPEG compressed BOSSBase containing 10,000 covers of
size 512x512. Results have verified that both the pooling method and the depth
of the CNNs are critical for performance. Results have also proved that a
20-layer CNN, in general, outperforms the most sophisticated feature-based
methods, but its advantage gradually diminishes on hard-to-detect cases. To
show that the performance generalizes to large-scale databases and to different
cover sizes, one experiment has been conducted on the CLS-LOC dataset of
ImageNet containing more than one million covers cropped to unified size of
256x256. The proposed 20-layer CNN has cut the error achieved by a CNN recently
proposed for large-scale JPEG steganalysis by 35%. Source code is available via
GitHub: https://github.com/GuanshuoXu/deep_cnn_jpeg_steganalysisComment: Accepted by IH&MMSec 2017. This is a personal cop
Steganographer Identification
Conventional steganalysis detects the presence of steganography within single
objects. In the real-world, we may face a complex scenario that one or some of
multiple users called actors are guilty of using steganography, which is
typically defined as the Steganographer Identification Problem (SIP). One might
use the conventional steganalysis algorithms to separate stego objects from
cover objects and then identify the guilty actors. However, the guilty actors
may be lost due to a number of false alarms. To deal with the SIP, most of the
state-of-the-arts use unsupervised learning based approaches. In their
solutions, each actor holds multiple digital objects, from which a set of
feature vectors can be extracted. The well-defined distances between these
feature sets are determined to measure the similarity between the corresponding
actors. By applying clustering or outlier detection, the most suspicious
actor(s) will be judged as the steganographer(s). Though the SIP needs further
study, the existing works have good ability to identify the steganographer(s)
when non-adaptive steganographic embedding was applied. In this chapter, we
will present foundational concepts and review advanced methodologies in SIP.
This chapter is self-contained and intended as a tutorial introducing the SIP
in the context of media steganography.Comment: A tutorial with 30 page
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