Attacks

Supported Attacks

The following table lists the supported attacks in BackdoorMBTI:

Modality	Attack	Visible	Pattern	Add	Sample Specific	paper
Image	AdaptiveBlend	Invisible	Global	Yes	No	REVISITING THE ASSUMPTION OF LATENT SEPARABILITY FOR BACKDOOR DEFENSES
Image	BadNets	Visible	Local	Yes	No	Badnets: Evaluating backdooring attacks on deep neural networks
Image	Blend	Invisible	Global	Yes	Yes	Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning
Image	Blind(under test)	Visible	Local	Yes	Yes	Blind Backdoors in Deep Learning Models
Image	BPP	Invisible	Global	Yes	No	Bppattack: Stealthy and efficient trojan attacks against deep neural networks via image quantization and contrastive adversarial learning
Image	DynaTrigger	Visible	Local	Yes	Yes	Dynamic backdoor attacks against machine learning models
Image	EMBTROJAN(under test)	Invisible	Local	Yes	No	An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks
Image	LC	Invisible	Global	No	Yes	Label-consistent backdoor attacks
Image	Lowfreq	Invisible	Global	Yes	Yes	Rethinking the Backdoor Attacks’ Triggers: A Frequency Perspective
Image	PNoise	Invisible	Global	Yes	Yes	Use procedural noise to achieve backdoor attack
Image	Refool	Invisible	Global	Yes	No	Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks
Image	SBAT	Invisible	Global	No	Yes	Stealthy Backdoor Attack with Adversarial Training
Image	SIG	Invisible	Global	Yes	No	A NEW BACKDOOR ATTACK IN CNNS BY TRAINING SET CORRUPTION WITHOUT LABEL POISONING
Image	SSBA	Invisible	Global	No	Yes	Invisible Backdoor Attack with Sample-Specific Triggers
Image	trojanNN(under test)	Visible	Local	Yes	Yes	Trojaning Attack on Neural Network
Image	ubw	Invisible	Global	Yes	No	Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protection
Image	WaNet	Invisible	Global	No	Yes	WaNet – Imperceptible Warping-Based Backdoor Attack
Text	AddSent	Visible	Local	Yes	No	A backdoor attack against LSTM-based text classification systems
Text	BadNets	Visible	Local	Yes	No	Badnets: Evaluating backdooring attacks on deep neural networks
Text	BITE	Invisible	Local	Yes	Yes	Textual backdoor attacks with iterative trigger injection
Text	LWP	Visible	Local	Yes	No	Backdoor Attacks on Pre-trained Models by Layerwise Weight Poisoning
Text	STYLEBKD	Visible	Global	No	Yes	Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer
Text	SYNBKD	Invisible	Global	No	Yes	Hidden Killer: Invisible Textual Backdoor Attacks with Syntactic Trigger
Audio	Baasv(under test)	-	Global	Yes	No	Backdoor Attack against Speaker Verification
Audio	Blend	-	Local	Yes	No	Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning
Audio	DABA	-	Global	Yes	No	Opportunistic Backdoor Attacks: Exploring Human-imperceptible Vulnerabilities on Speech Recognition Systems
Audio	GIS	-	Global	No	No	Going in style: Audio backdoors through stylistic transformations
Audio	UltraSonic	-	Local	Yes	No	Can You Hear It? Backdoor Attacks via Ultrasonic Triggers