Multimedia Forensics - Revision history

Maarab: /* Publications */

2024-08-22T18:49:03Z

Publications

Maarab: /* Code and Datasets */

2024-08-22T18:48:41Z

Code and Datasets

Maarab: /* Publications */

2024-08-22T18:48:27Z

Publications

Maarab: /* Code and Datasets */

2024-08-22T18:45:58Z

Code and Datasets

Maarab: /* People */

2024-08-22T18:43:54Z

People

Mhefeeda: /* People */

2022-12-22T21:47:49Z

People

Mhefeeda: /* People */

2022-12-22T21:46:41Z

People

Maarab: /* Code and Datasets */

2022-10-24T17:44:36Z

Code and Datasets

Mhefeeda at 20:16, 6 August 2021

2021-08-06T20:16:15Z

Maarab: Created page with "This project addresses multimedia forensics, including detecting makeup attacks in biometrics systems to deepfake video and generated audio detection. == People == * [https:..."

2021-03-03T19:22:09Z

Created page with "This project addresses multimedia forensics, including detecting makeup attacks in biometrics systems to deepfake video and generated audio detection. == People == * [https:..."

New page

This project addresses multimedia forensics, including detecting makeup attacks in biometrics systems to deepfake video and generated audio detection.

== People ==
* [https://maarab-sfu.github.io Mohammad Amin Arab]
* Puria Azadi
* [https://www.cs.sfu.ca/~mhefeeda/ Mohamed Hefeeda]

== Revealing True Identity: Detecting Makeup Attacks in Face-based Biometric Systems ==

''' Abstract '''

Face-based authentication systems are among the most commonly used biometric systems, because of the ease of capturing face images at a distance and in non-intrusive way. These systems are, however, susceptible to various presentation attacks, including printed faces, artificial masks, and makeup attacks. In this paper, we propose a novel solution to address makeup attacks, which are the hardest to detect in such systems because makeup can substantially alter the facial features of a person, including making them appear older/younger by adding/hiding wrinkles, modifying the shape of eyebrows, beard, and moustache, and changing the color of lips and cheeks. In our solution, we design a generative adversarial network for removing the makeup from face images while retaining their essential facial features and then compare the face images before and after removing makeup. We collect a large dataset of various types of makeup, especially malicious makeup that can be used to break into remote unattended security systems. This dataset is quite different from existing makeup datasets that mostly focus on cosmetic aspects. We conduct an extensive experimental study to evaluate our method and compare it against the state-of-the art using standard objective metrics commonly used in biometric systems as well as subjective metrics collected through a user study. Our results show that the proposed solution produces high accuracy and substantially outperforms the closest works in the literature.

* M. Arab, P. Azadi Moghadam, M. Hussein, W. Abd-Almageed, M. Hefeeda. Revealing True Identity: Detecting Makeup Attacks in Face-based Biometric Systems. In Proceedings of the 28th ACM International Conference on Multimedia 2020.

← Older revision		Revision as of 18:49, 22 August 2024
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	* M. A. Arab, A. Ghorbanpour, and M. Hefeeda. [https://www2.cs.sfu.ca/~mhefeeda/Papers/mmsys24_flexmark.pdf FlexMark: Adaptive Watermarking Method for Images], In Proc. of Multimedia Systems Conference (MMSys'24), Bari, Italy, April 2024.		* M. A. Arab, A. Ghorbanpour, and M. Hefeeda. [https://www2.cs.sfu.ca/~mhefeeda/Papers/mmsys24_flexmark.pdf FlexMark: Adaptive Watermarking Method for Images], In Proc. of Multimedia Systems Conference (MMSys'24), Bari, Italy, April 2024.

−	* M. Arab, P. Azadi Moghadam, M. Hussein, W. Abd-Almageed, and M. Hefeeda. [https://www2.cs.sfu.ca/~mhefeeda/Papers/mm20_makeup.pdf Revealing True Identity: Detecting Makeup Attacks in Face-based Biometric Systems], In Proc. of ACM Multimedia Conference (MM'20), Seattle, WA, October 2020.	+	* M. A. Arab, P. Azadi Moghadam, M. Hussein, W. Abd-Almageed, and M. Hefeeda. [https://www2.cs.sfu.ca/~mhefeeda/Papers/mm20_makeup.pdf Revealing True Identity: Detecting Makeup Attacks in Face-based Biometric Systems], In Proc. of ACM Multimedia Conference (MM'20), Seattle, WA, October 2020.

@@ Line 36: / Line 36: @@
 == Code and Datasets ==
 https://github.com/maarab-sfu/revealing-true-identity
 == Publications ==

← Older revision		Revision as of 18:48, 22 August 2024
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	== Publications ==		== Publications ==
		+	* M. A. Arab, A. Ghorbanpour, and M. Hefeeda. [https://www2.cs.sfu.ca/~mhefeeda/Papers/mmsys24_flexmark.pdf FlexMark: Adaptive Watermarking Method for Images], In Proc. of Multimedia Systems Conference (MMSys'24), Bari, Italy, April 2024.
		+
	* M. Arab, P. Azadi Moghadam, M. Hussein, W. Abd-Almageed, and M. Hefeeda. [https://www2.cs.sfu.ca/~mhefeeda/Papers/mm20_makeup.pdf Revealing True Identity: Detecting Makeup Attacks in Face-based Biometric Systems], In Proc. of ACM Multimedia Conference (MM'20), Seattle, WA, October 2020.		* M. Arab, P. Azadi Moghadam, M. Hussein, W. Abd-Almageed, and M. Hefeeda. [https://www2.cs.sfu.ca/~mhefeeda/Papers/mm20_makeup.pdf Revealing True Identity: Detecting Makeup Attacks in Face-based Biometric Systems], In Proc. of ACM Multimedia Conference (MM'20), Seattle, WA, October 2020.

@@ Line 37: / Line 37: @@
 == Code and Datasets ==
 https://github.com/maarab-sfu/revealing-true-identity
 == Publications ==
 * M. Arab, P. Azadi Moghadam, M. Hussein, W. Abd-Almageed,  and M. Hefeeda. [https://www2.cs.sfu.ca/~mhefeeda/Papers/mm20_makeup.pdf Revealing True Identity: Detecting Makeup Attacks in Face-based Biometric Systems], In Proc. of ACM Multimedia Conference (MM'20), Seattle, WA, October 2020.

← Older revision		Revision as of 18:43, 22 August 2024
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	* Puria Azadi (graduated)		* Puria Azadi (graduated)
	* [https://www.cs.sfu.ca/~mhefeeda/ Mohamed Hefeeda]		* [https://www.cs.sfu.ca/~mhefeeda/ Mohamed Hefeeda]
		+
		+
		+	'''FlexMark: Adaptive Watermarking Method for Images'''
		+
		+	{\|
		+	\| Most current watermarking methods offer low and fixed capacity, which means they can only embed small-size watermarks into images. Additionally, they are typically robust to only a small subset of the known image transformations (aka distortions) that occur during the processing, transmission, and storage of images. These shortcomings limit their adoption in many practical multimedia applications. We propose FlexMark, a robust and adaptive watermarking method for images, which achieves a better capacity-robustness trade-off than current methods and can easily be used for different applications. FlexMark categorizes and models the fundamental aspects of various image transformations, enabling it to achieve high accuracy in the presence of many practical transformations.
		+
		+	FlexMark introduces new ideas to further improve the performance, including double-embedding of the input message, employing self-attention layers to identify the most suitable regions in the image to embed the watermark bits, and utilization of a discriminator to improve the visual quality of watermarked images. In addition, FlexMark offers a parameter, α, to enable users to control the trade-off between robustness and capacity to meet the requirements of different applications. We implement FlexMark and assess its performance using datasets commonly used in this domain. Our results show that FlexMark is robust against a wide range of image transformations, including ones that were never seen during its training, which shows its generality and practicality. Our results also show that FlexMark substantially outperforms the closest methods in the literature in terms of capacity and robustness.
		+	\|\|
		+	[[File:FlexMark.jpg\|thumb\|right]]
		+	\|}
		+

@@ Line 3: / Line 3: @@
 == People ==
 * [https://maarab-sfu.github.io Mohammad Amin Arab]
-* Puria Azadi
+* Ali Ghorbanpour
 * [https://www.cs.sfu.ca/~mhefeeda/ Mohamed Hefeeda]

@@ Line 10: / Line 10: @@
 '''Revealing True Identity: Detecting Makeup Attacks in Face-based Biometric Systems'''
-Face-based authentication systems are among the most commonly used biometric systems, because of the ease of capturing face images at a distance and in non-intrusive way. These systems are, however, susceptible to various presentation attacks, including printed faces, artificial masks, and makeup attacks. In this paper, we propose a novel solution to address makeup attacks, which are the hardest to detect in such systems because makeup can substantially alter the facial features of a person, including making them appear older/younger by adding/hiding wrinkles, modifying the shape of eyebrows, beard, and moustache, and changing the color of lips and cheeks. In our solution, we design a generative adversarial network for removing the makeup from face images while retaining their essential facial features and then compare the face images before and after removing makeup. We collect a large dataset of various types of makeup, especially malicious makeup that can be used to break into remote unattended security systems. This dataset is quite different from existing makeup datasets that mostly focus on cosmetic aspects. We conduct an extensive experimental study to evaluate our method and compare it against the state-of-the art using standard objective metrics commonly used in biometric systems as well as subjective metrics collected through a user study. Our results show that the proposed solution produces high accuracy and substantially outperforms the closest works in the literature.
+{|
 == Code and Datasets ==

@@ Line 1: / Line 1: @@
-This project addresses multimedia forensics, including detecting makeup attacks in biometrics systems to deepfake video and generated audio detection.
+Recent advances in machine learning have made it easier to create fake images and videos. Users and computing systems are facing increasing difficulties in differentiating forged contents from original ones. In this project, we focus on various aspects of detecting fake content, including detecting makeup attacks in biometrics systems to identifying forged videos and images.
 == People ==
@@ Line 7: / Line 6: @@
 * [https://www.cs.sfu.ca/~mhefeeda/ Mohamed Hefeeda]
-''' Abstract '''
 Face-based authentication systems are among the most commonly used biometric systems, because of the ease of capturing face images at a distance and in non-intrusive way. These systems are, however, susceptible to various presentation attacks, including printed faces, artificial masks, and makeup attacks. In this paper, we propose a novel solution to address makeup attacks, which are the hardest to detect in such systems because makeup can substantially alter the facial features of a person, including making them appear older/younger by adding/hiding wrinkles, modifying the shape of eyebrows, beard, and moustache, and changing the color of lips and cheeks. In our solution, we design a generative adversarial network for removing the makeup from face images while retaining their essential facial features and then compare the face images before and after removing makeup. We collect a large dataset of various types of makeup, especially malicious makeup that can be used to break into remote unattended security systems. This dataset is quite different from existing makeup datasets that mostly focus on cosmetic aspects. We conduct an extensive experimental study to evaluate our method and compare it against the state-of-the art using standard objective metrics commonly used in biometric systems as well as subjective metrics collected through a user study. Our results show that the proposed solution produces high accuracy and substantially outperforms the closest works in the literature.
-* M. Arab, P. Azadi Moghadam, M. Hussein, W. Abd-Almageed,  M. Hefeeda. Revealing True Identity: Detecting Makeup Attacks in Face-based Biometric Systems. In Proceedings of the 28th ACM International Conference on Multimedia 2020.
+== Code and Datasets ==