Public Invesigative Research On: New African Magazine

Framing of the Sino–Africa relationship in diasporic/pan-African news magazines

Abstract:

Using the New African magazine and the African Business magazine as its case study, this paper examines the coverage of the Sino–African relationship in the pan-African news magazines of the African diaspora. The objectives of the study are twofold. The first objective is to provide insights on the level of coverage of Sino–African relationship by the diasporic African media. The second objective is to understand how the presence of China in the socioeconomic and political spheres of many African countries is portrayed. The findings show that the African Business provides extensive coverage of the multidimensional complexity of China's presence in Africa, as opposed to the episodic-oriented coverage that Sino–African relationships get in the New African magazine. While the thematic framing of the Sino–African relationships are similar in both magazines, the New African magazine takes a dogmatic approach in its portrayal of the relationship in an overly positive manner in comparison to the African Business's more contextual and nuanced coverage.

Disclosure:

IC Publications is a publishing house founded in 1957 in Paris by Afif Ben Yedder. Its headquarters are now based in London Farringdon. 

The Group's magazines are read by 2.6 million people in over 100 countries. (IC Publications. (Tokunbo, 2016).

In English, IC publishes New African, African Business, African Banker, New African Woman and, in French, Le Magazine De L’Afrique, Le Magazine Des Dirigeants Africains, Le Magazine De La Banque Et De La Finance and Femme Africaine. Its flagship title, African Business Magazine, is a widely respected source of business and investment news across the continent.


Annotated Bibliography:

Tokunbo Ojo (2016). "Framing of the Sino–Africa relationship in diasporic/pan-African news magazines". Chinese Journal of Communication. 9 (1): 38-55. doi:10.1080/17544750.2015.1049628

Tokunbo Ojo earned a Ph.D. from the University of Queensland. His teaching and research interests are in global media studies, development communication, journalism studies and political communication. His works have appeared in peer-reviewed journals such as the Journalism: Theory, Practice & Criticism; Journal of Communication for Development and Social Change; Journal of Information Technology Impact; Information Development, and International Journal of Education and Development using ICT (IJEDICT). In addition to published peer-reviewed journal articles, he has published book chapters in the scholarly edited books such as Reimagining Development Communication in Africa (edited by Chuka Onwumechili & Ikechukwu Ndolo) and Sustainability, Participation and Culture in Communication: Theory & Praxis (edited by Jan Servaes).

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Committee: Drs. Hamed Pirsiavash (Advisor/Co-Chair), Anupam

Joshi (Chair), Tim Oates, Tom Goldstein (UMCP), Dr. Pin-Yu Chen (IBM)

Deep Neural Networks (DNNs) have become the standard building block in numerous machine learning applications, including computer vision, speech recognition, machine translation, and robotic manipulation, achieving state-of-the-art performance on complex tasks. The widespread success of these networks has driven their deployment in sensitive domains like health care, finance, autonomous driving, and defense-related applications. 

However, DNNs are vulnerable to adversarial attacks. An adversary is a person with malicious intent whose goal is to disrupt the normal functioning of a machine learning pipeline. Research has shown that an adversary can tamper with the training process of a model by injecting misrepresentative data (poisons) into the training set. Moreover, if provided control over the training process as a third party, they can deliver a model to a victim, which deviates from normal behavior. These are called backdoor attacks. The manipulation is done in a way that the victim's model will malfunction only when a trigger is pasted on a test input. For instance, a backdoored model in a self-driving car might work accurately for days before it suddenly fails to detect a pedestrian when the adversary decides to exploit the backdoor. Vulnerability to backdoor attacks is dangerous when deep learning models are deployed in safety-critical applications.    

 

This dissertation studies ways in which state-of-the-art deep learning methods for computer vision are vulnerable to backdoor attacks and proposes defense methods to remedy the vulnerabilities. We push the limits of our current understanding of backdoors and address the following research questions.  

Can we design practical backdoor attacks? We propose Hidden Trigger Backdoor Attack - a novel clean-label backdoor attack where the poisoned images do not contain a visible trigger. This enables the attacker to keep the trigger hidden until its use at test-time. We believe such practical attacks reveal an important vulnerability of deep learning models. These flaws need to be studied extensively before deploying models in critical real-world applications.

Is it secure to train models on large-scale public data? Self-supervised learning (SSL) methods for vision have utilized large-scale unlabeled data to learn rich visual representations. These methods use public images downloaded from the web, e.g., Instagram-1B and Flickr image datasets. We show that if a small part of the unlabeled training data is poisoned, SSL methods are vulnerable to backdoor attacks. Backdoor attacks are more practical in self-supervised learning, since the use of large unlabeled data makes data inspection to remove poisons prohibitive. Hence, using large and diverse data to remove data biases and reduce labeling costs might unknowingly set up avenues for adversarial manipulation. Practitioners must make informed choices when choosing training data for machine learning models.

Can we design efficient and generalizable backdoor detection methods? Methods to detect backdoors in trained models have relied on analysis of model behavior on carefully chosen inputs. But often, this analysis has to be repeated for a new model. We propose a backdoor detection method that optimizes for a set of images which, when forwarded through any model, indicates successfully whether the model contains a backdoor. Our "litmus" test for backdoored models improves on state-of-the-art methods without requiring access to clean data during detection. It is computationally efficient and generalizes to new triggers as well as new architectures. We effectively detect backdoor attacks on thousands of networks with different architectures trained on four benchmark datasets, namely the German Traffic Sign Recognition Benchmark (GTSRB), MNIST, CIFAR10, and Tiny-ImageNet. This will serve as a benchmark for future research.

Do architectural design choices contribute to backdoor robustness? We find an intriguing difference between Vision Transformers and Convolutional Nets; interpretation algorithms effectively highlight the backdoor trigger on test images for transformers but not for CNNs when attacked with Hidden Trigger Backdoor Attacks. Based on this observation, we find that a test-time image blocking defense reduces attack success rate by a large margin for transformers. We show that such blocking mechanisms can be incorporated during the training process to improve robustness even further. We believe our findings will encourage the community to make better design choices in developing novel architectures robust to backdoor attacks.

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Cybersecurity is rich with analogies, from keys and locks to Trojan horses. We look for the "needle in the haystack" and "evict malware resident on our systems." We debate "baked-in" versus "bolted-on" security. We do not mean all these things literally, of course. The language and analogies we use in this field are borrowed from many different domains. Analogies can help explain basic cybersecurity concepts, but too often they omit or overgeneralize important details. They can mislead, sometimes deliberately, because the experience they purport to connect might be out of proportion. Despite their shortcoming and imprecision, using an analogy or an abstraction might be helpful in appropriate situations. Using analogies, abstractions, and metaphors shapes technology's development, practice, and policies. The analogies are more than simple figures of speech. They have a normative dimension; sometimes, they can be used to help the imaginary shape reality. 

This talk explores the use and misuse of analogies and metaphors across cybersecurity. We consider analogies from the physical world, medicine and biology, war and military, and law before discussing tips for avoiding pitfalls in using analogies and metaphors.  This talk is adapted from material in the forthcoming book Cybersecurity Myths and Misconceptions: Avoiding the Hazards and Pitfalls That Derail Us by Drs. Spafford, Metcalf, and Dykstra. The book presents 175+ common myths and misconceptions held by users, leaders, and cybersecurity professionals, along with tips for how to avoid them.

Josiah Dykstra is a Technical Fellow in the Cybersecurity Collaboration Center at the National Security Agency (NSA) and the owner of Designer Security, LLC. In 2013, he earned his PhD in computer science from UMBC studying cloud forensics with Dr. Sherman. He is interested in cybersecurity science, especially where humans intersect with technology. He has studied stress in hacking, action bias in incident response, and economics of cyber threat intelligence. Dr. Dykstra is a frequent author and speaker, including at the Black Hat and RSA Conference. He received the CyberCorps Scholarship for Service (SFS) fellowship and is one of seven people in the SFS Hall of Fame. In 2017, he received the Presidential Early Career Award for Scientists and Engineers (PECASE) from then President Barack Obama.  Dr. Dykstra is a Fellow of the American Academy of Forensic Sciences and a Distinguished Member of the Association for Computing Machinery (ACM). He is the author of numerous research papers and two books.

Host: Alan T. Sherman, sherman@umbc.edu. Support for this event was provided in part by the National Science Foundation under SFS grant DGE-1753681.  The UMBC Cyber Defense Lab meets biweekly Fridays 12-1pm. All meetings are open to the public.  Upcoming CDL meetings: Dec 2, Peter Peterson (UMN Duluth), Adversarial Thinking Dec 20, 10:30am-12:30pm, 3 In-Person presentations from the UMBC fall 2022 cybersecurity research class, location TBA SFS/CySP Research Study: January 2-6, 2023 (tentative)