Welcome to the 5th ACM Workshop on Information Hiding and Multimedia Security Workshop -- IH&MMSec'17 in Philadelphia, PA, held June 20-21, 2017.
In response to our call for papers, 34 excellent papers were submitted from authors throughout North America, Europe, and Asia. The best 18 of these papers were accepted (53% acceptance rate) and assembled into a strong technical program. The accepted papers cover the fields of steganography and steganalysis in digital media, multimedia forensics, digital watermarking, data hiding in natural language, deep learning approaches to both forensics and steganalysis.
We sincerely thank all the submitting authors for their contributions, and the reviewers for their invaluable help. We expect the selected papers to be of wide interest to researchers working in the field and to participants from industry and from government institutions.
The technical program also includes two invited keynote speakers. The first presentation is given by Dr. Anupam Das from Carnegie Mellon University on the topic of using motion sensors in smartphones to track users. The second presentation is given by Dr. Rachel Greenstadt from Drexel University on the topic of how stylometry and machine learning can be used to determine the author of both written documents and software.
As usual, the workshop is structured into three days with the afternoon of the second day devoted to a social event. The social event is designed to promote discussions and to help establish relationships for future collaboration among participants. Also, at the end of the second day before the start of the social event, time is reserved for a rump session during which the participants are encouraged to share their work in progress, discuss unpublished results, demo new products, and make relevant announcements.
A great team effort put together the technical program. The Program Committee assisted by 29 external reviewers provided timely and high-quality reviews. A double-blind review process was used to ensure fairness. Each paper was carefully read and appraised by at least three reviewers, however the majority of papers were reviewed by four reviewers. To let the Program Chairs select the best quality and relevant work, papers with conflicting reviews were discussed at length. We thank all participants for their help in putting together this great program.
Proceeding Downloads
Every Move You Make: Tracking Smartphone Users through Motion Sensors
Online users are increasingly being subjected to privacy-invasive tracking across the web for advertisement and surveillance purposes, using IP addresses, cookies, and browser fingerprinting. As web browsing activity shifts to mobile platforms such as ...
Information-theoretic Bounds of Resampling Forensics: New Evidence for Traces Beyond Cyclostationarity
Although several methods have been proposed for the detection of resampling operations in multimedia signals and the estimation of the resampling factor, the fundamental limits for this forensic task leave open research questions. In this work, we ...
Countering Anti-Forensics of Lateral Chromatic Aberration
Research has shown that lateral chromatic aberrations (LCA), an imaging fingerprint, can be anti-forensically modified to hide evidence of cut-and-paste forgery. In this paper, we propose a new technique for securing digital images against anti-forensic ...
Modeling Attacks on Photo-ID Documents and Applying Media Forensics for the Detection of Facial Morphing
Since 2014, a novel approach to attack face image based person verification designated as face morphing attack has been actively discussed in the biometric and media forensics communities. Up until that point, modern travel documents were considered to ...
The Square Root Law of Steganography: Bringing Theory Closer to Practice
There are two interpretations of the term "square root law of steganography". As a rule of thumb, that the secure capacity of an imperfect stegosystem scales only with the square root of the cover size (not linearly as for perfect stegosystems), it acts ...
Nonlinear Feature Normalization in Steganalysis
In this paper, we propose a method for normalization of rich feature sets to improve detection accuracy of simple classifiers in steganalysis. It consists of two steps: 1) replacing random subsets of empirical joint probability mass functions (co-...
Improving GFR Steganalysis Features by Using Gabor Symmetry and Weighted Histograms
The GFR (Gabor Filter Residual) features, built as histograms of quantized residuals obtained with 2D Gabor filters, can achieve competitive detection performance against adaptive JPEG steganography. In this paper, an improved version of the GFR is ...
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 ...
JPEG-Phase-Aware Convolutional Neural Network for Steganalysis of JPEG Images
Detection of modern JPEG steganographic algorithms has traditionally relied on features aware of the JPEG phase. In this paper, we port JPEG-phase awareness into the architecture of a convolutional neural network to boost the detection accuracy of such ...
Audio Steganalysis with Convolutional Neural Network
In recent years, deep learning has achieved breakthrough results in various areas, such as computer vision, audio recognition, and natural language processing. However, just several related works have been investigated for digital multimedia forensics ...
Using Stylometry to Attribute Programmers and Writers
In this talk, I will discuss my lab's work in the emerging field of adversarial stylometry and machine learning. Machine learning algorithms are increasingly being used in security and privacy domains, in areas that go beyond intrusion or spam ...
Towards Imperceptible Natural Language Watermarking for German
Watermarking natural language is still a challenge in the domain of digital watermarking. Here, only the textual information must be used as a cover. No format changes or modified illustrations are accepted. Still, natural language watermarking (NLW) ...
Text Steganography with High Embedding Rate: Using Recurrent Neural Networks to Generate Chinese Classic Poetry
We propose a novel text steganography method using RNN Encoder-Decoder structure to generate quatrains, one genre of Chinese poetry. Compared to other text-generation based steganography methods which have either very low embedding rate or flaws in the ...
Audio Reversible Watermarking Scheme in the intDCT Domain with Modified Prediction Error Expansion
Reversible watermarking schemes (RWS) allow the restoration of the original signals after the watermarks are extracted. Most RWS for audio signals use time-domain for information hiding, although their transparency is hard to maintain for high embedding ...
A Minimum Distortion: High Capacity Watermarking Technique for Relational Data
In this paper, a new multi-attribute and high capacity image-based watermarking technique for relational data is proposed. The embedding process causes low distortion into the data considering the usability restrictions defined over the marked relation. ...
A Steganalytic Algorithm to Detect DCT-based Data Hiding Methods for H.264/AVC Videos
This paper presents an effective steganalytic algorithm to detect Discrete Cosine Transform (DCT) based data hiding methods for H.264/AVC videos. These methods hide covert information into compressed video streams by manipulating quantized DCT ...
Combined and Calibrated Features for Steganalysis of Motion Vector-Based Steganography in H.264/AVC
This paper presents a novel feature set for steganalysis of motion vector-based steganography in H.264/AVC. First, the influence of steganographic embedding on the sum of absolute difference (SAD) and the motion vector difference (MVD) is analyzed, and ...
A Generic Approach Towards Image Manipulation Parameter Estimation Using Convolutional Neural Networks
Estimating manipulation parameter values is an important problem in image forensics. While several algorithms have been proposed to accomplish this, their application is exclusively limited to one type of image manipulation. These existing techniques ...
Recasting Residual-based Local Descriptors as Convolutional Neural Networks: an Application to Image Forgery Detection
Local descriptors based on the image noise residual have proven extremely effective for a number of forensic applications, like forgery detection and localization. Nonetheless, motivated by promising results in computer vision, the focus of the research ...
Image Forensics Based on Transfer Learning and Convolutional Neural Network
There have been a growing number of interests in using the convolutional neural network(CNN) in image forensics, where some excellent methods have been proposed. Training the randomly initialized model from scratch needs a big amount of training data ...
Cited By
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Zheng W, Zhou F, Ling X, Qin C and Zhou H (2024). FETNet: frequency-enhanced transformer network for face forgery detection International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 10.1117/12.3035427, 9781510681514, (145)
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Cai Z, Wei W, Meng F, Liu C, Xie Y, Jiang X, Tao W and Zeng D (2022). Face tampering detection based on spatiotemporal attention residual network Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 10.1117/12.2644654, 9781510657564, (153)
Index Terms
- Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security
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Acceptance Rates
Year | Submitted | Accepted | Rate |
---|---|---|---|
IH&MMSec '18 | 40 | 18 | 45% |
IH&MMSec '17 | 34 | 18 | 53% |
IH&MMSec '16 | 61 | 21 | 34% |
IH&MMSec '15 | 45 | 20 | 44% |
IH&MMSec '14 | 64 | 24 | 38% |
IH&MMSec '13 | 74 | 27 | 36% |
Overall | 318 | 128 | 40% |