Quantum Man-in-the-middle Attacks: a Game-theoretic Approach with Applications to Radars
Abstract
The detection and discrimination of quantum states serve a crucial role in quantum signal processing, a discipline that studies methods and techniques to process signals that obey the quantum mechanics frameworks. However, just like classical detection, evasive behaviors also exist in quantum detection. In this paper, we formulate an adversarial quantum detection scenario where the detector is passive and does not know the quantum states have been distorted by an attacker. We compare the performance of a passive detector with the one of a non-adversarial detector to demonstrate how evasive behaviors can undermine the performance of quantum detection. We use a case study of target detection with quantum radars to corroborate our analytical results.
Index Terms— Quantum detection, quantum man-in-the-middle attack, quantum radars,
1 Introduction
The past two decades have witnessed a booming development of theories and applications of quantum systems. The discipline of quantum signal processing (QSP) [1] was founded under the construction of quantum mechanics frameworks [2] and studies the manipulation, detection and restoration of quantum signals. One important branch of study of quantum systems is the detection and discrimination of states. In quantum signal processing, detecting the quantum state with good accuracy lays a foundation for many signal processing problems including the reconstruction of the original quantum state.
One challenge in designing quantum detection systems results from undesirable interactions [3] between the target mixed states and the exogenous environments. In particular, the quantum states can be manipulated adversarially by malicious attackers, who undermine the detection performance significantly. For instance, a quantum man-in-the-middle attack [4] jeopardizes the encryption process in quantum key distribution. There is a need to formulate the adversarial manipulations of quantum states for quantum detection systems.
In this paper, we develop a game-theoretical framework to study how a strategic attacker can undermine the performance of a quantum detector by distorting the quantum states produced by the quantum system. Game-theoretic frameworks have been widely implemented in studying attacker-defender relationships in cyber-security [5, 6]. In a generic quantum detection characterized by hypothesis testing scenario [7], the detector (He) receives a sample of quantum states drawn from an unknown ensemble characterized by a density operator. The detector aims to make a decision regarding the genuine density operator that produces the quantum state. In our adversarial scenario, an attacker (She) observes the true density operator, intercepts the original quantum states, and sends strategically distorted quantum states to the ‘naive’ detector, who applies the decision rule as if the received quantum states were untainted. We model the relationship between the attacker and the naive detector as a Stackelberg game [8], where the attacker plays the role of a leader and the detector the follower.
Our analysis sheds light on fundamental limits on a quantum detector’s performance in adversarial situations. We observe that by varying the threshold, one can depict the detection rate and false alarm rate of a naive quantum detector in terms of a receiver-operational-characteristic (ROC) curve [7], which illustrates the detector performance. Compared to the curve for non-adversarial quantum detectors, the naive detector’s performances deteriorate significantly as the attacker attenuate the component of the mixed state that is projected on the detection region. Our results have clear implications for developing improved and attack-aware quantum detection systems that can combat strategically-designed quantum operations upon states. Our contributions are twofold:
The rest of the paper is organized as follows. In Section 2, we formulate man-in-the-middle-attack as a Stackelberg game and compute his optimal strategies. In Section 3, we illustrate our formulation with a case study in target detection using quantum radars of a particular type. We conclude in Section 4.
Notations
We denote as the Hilbert space (over the set of real numbers ) and as its dual space. We inherit Dirac’s bra-ket notations [9] to denote generic quantum states: . Let be the space of all positive, Hermitian and bounded operators from to itself. Let be the subset of such trace of its operators is . In addition, we denote as the space of projection-valued measurements [2]. We denote as the identity operator. For any operator , we denote its conjugate transpose as .
2 The formulation of a quantum man-in-the-middle (MITM) attacker
We begin by considering a non-adversarial detection formulation based on quantum hypothesis testing introduced in [7]. Suppose that there is an unknown target quantum system characterized by a density operator . We consider the binary hypothesis testing scenario, assuming that the nature of the system specifies two possible choices for or , each of which forms a hypothesis as follows:
(1) |
with and . We assume that span the Hilbert space of dimension . Notice that we do not assume that commute; i.e. may be two different bases of . The target system produces quantum states which are collected by a detector (He), who wants to arrive at a decision rule on which is the genuine characterization of the target system in (1): when he thinks the hypothesis holds true. According to the measurement postulate of quantum mechanics [2], the detector decides by applying a measurement upon the received quantum state : . The decision rule , or equivalently the measurement , leads to a detection rate and a false alarm rate as follows:
(2) |
In Bayesian hypothesis testing formulation, the detector knows the probabilities that hold true are () respectively. Referring to [7] and denoting , detector’s optimal solution measurement can be designed as follows:
(3) |
where are orthogonal eigenstates of with eigenvalues .

2.1 Quantum man-in-the-middle attack: a Stackelberg game formulation
We now introduce the scenario (as in Figure 1) of adversarial quantum detection in which an attacker (She) stands between the target system and detector, intercepts the quantum state from the target systems and sends out strategically distorted signal to the detector to undermine his performance. We assume that the detector is passive and ‘naive’ about the distortion and designs his optimal measurement operation as if the quantum state were directly generated from the target system. Upon receiving , the detector designs an optimal decision rule by measuring the quantum state from attacker with measurement, denoted as as follows:
Thus we let the detector’s cost function be the Bayesian risk, which is a weight sum of counterfactual miss rate and counterfactual false alarm rate in (2) as if the quantum state were untainted:
(4) | ||||
which leads to his optimal measurement as in (3). The attacker, after observing the true hypothesis ( or ) on the target systems, obtains the quantum state . Based on his observation on the true density state ( or ), the attacker designs quantum noisy operations acting upon the received state to create a distorted quantum state . According to [11], we formulate the resulting noisy density operators as the ‘operator-sum’ representation as follows:
(5) |
We could treat the space in (5) as the attacker’s action space. Yet we argue through the following lemma that it is equivalent to characterize the attacker’s strategies as a pair of density operators .
Lemma 1 (Equivalency of attacker’s strategies)
Let be the two density operators in (1). Then, for any there exist operations of the operator-sum representation such that .
Knowing detector’s strategy (3) the detector designs her optimal strategies by minimizing her utility function as follows:
(6) | ||||
where we adopt the Von-Neumann relative entropy [11] for any two density operators as , and is the solution to detector’s optimization problem (4).
In the objective function of (6), the attacker trades off between undermining the genuine detection rate (the first term) and minimizing the loss incurred from distorting quantum states through noisy gates (the second term). Such a loss term originates from the detector’s awareness of the distortion if the received quantum states deviate significantly from the ‘normal’ ones. The parameter is a regularization parameter characterizing the attacker’s intentions.
Summarizing the formulations raised in (4) and (6), we arrive at the game between passive quantum detector and the attacker , which is a Stackelberg game [8] defined as follows.
Definition 1
We define the relationship between the passive quantum detector and the attacker as a Stackelberg game with the following tuples
(7) |
where refers to the set of players; refers to the set of true hypotheses specified in (1); is the Hilbert space; the set in (5) characterizes the attacker’s strategy space; the set of measurements characterizes the detector’s space of decision rules. The functions specified in (4) and (6) the objectives of the attacker and the detector, respectively.
We now state the attacker’s optimal design of distorted mixed densities into the following proposition:
Proposition 1 (Attacker’s optimal strategies)
Let be the Stackelberg game between the attacker and the detector (the passive quantum detector) as mentioned in definition 1. Then the attacker’s optimal strategies are expressed as follows:
(8) | ||||
(9) |
We have several remarks on the attacker’s and the detector’s optimal strategies. First, as , the penalty for distorting the mixed state vanishes, and the attacker manages to suppress all the components in . On the other hand, when , the attacker’s optimal strategies , meaning the attacker does not distort the original density operator at all because the penalty for distorting the mixed states becomes infinitely high.
Secondly, we can interpret the attacker’s optimal manipulation of mixed density states as in Proposition (1) as the attenuation of the components of the original quantum state upon the subspace spanned by the base states lying in the region of detection. Applying Baker-Campbell-Hausdorf formula to the nominator of the RHS, of (9) we obtain
(10) |
where “” refers to additional terms involving iterative Poisson brackets of and . The product indicates the attenuation of the original mixed state on the subspace controlled by the parameter . The the remaining factor , where the commutator is included in the exponent, implies that the quantum uncertainty principle also affects attacker’s optimal strategies.
Thirdly, when commute (a sufficient condition would be commute), the attacker’s optimal strategies (8)(9) reduce to the strategies in classical Stackelberg hypothesis testing games as formulated in Section II of [12].
We can now compute the genuine detection rate under the attacker’s manipulation:
(11) |
where is obtained from (9). We have the following statement:
Proposition 2
Proposition 2 characterizes an upper and lower bound of the genuine detection rate under strategically designed quantum operations.
3 Case study: target detection using quantum radars
We now apply the formulation of the quantum man-in-the-middle attack discussed in Section 2 to study spoofing in quantum radar detection. A quantum radar is a standoff detection system using photons to explore some quantum phenomena to strengthen its capacity to detect targets of interest. In this section, we assume that our quantum radar generates non-entangled, monochromatic, coherent [13] photonic quantum states subject to noise in line with Llyod’s theory [14].
We illustrate the MITM attack scheme in Figure 1: a quantum radar (detector) determines on the absence or presence of the target object based on the reflective photon-based quantum states. An attacker blocks the transmission of reflective quantum signals, manipulates them through noisy quantum gates selected from the action space in (5), which can be implemented using photonic quantum circuits [15], and sends out manipulated quantum signals. We use the mean number representation to characterize photonic quantum states from the reflective signals [16]. We associate the hypotheses with the mixed state of reflective signals under the absence or presence of the target object in Figure 1, respectively as follows:
(13) | ||||
where refers to the reflective index, and characterizes the noise of the environment.
Based on the true hypothesis and the input quantum state, the spoofer/attacker produces distorted quantum mixed densities as in (6). We choose in (13) and depict in Figure 2 the relationship between the detection rate in terms the mean number of photons under attacks parameterized by different choices of . We observe that no matter the distortion level, the quantum detector reaches its worst detection rates when the mean photon number is around , and its second worst rate is at . Nevertheless, different distortion levels cause a contrast in detection rates among all mean photon number states. In Figure 3, we plot the receiver-operational-characteristic (ROC) curves of classical quantum detectors and passive quantum detectors when the attacker’s manipulation strategy is controlled under different choices of . We observe that the passive quantum detectors perform worse than the classical quantum detectors because the spoofer manipulate the states to undermine the detection rates. Lower values of lead to stronger distortion of states and thus worse performance in terms of the ROC curves.


4 Conclusion
We have formulated the quantum man-in-the-middle attack under the Stackelberg framework to study the fundamental limit of the reduction of the detection performance. We characterize the attacker’s optimal strategies as a suppression of the target’s density operator on the region of detection, which is reshaped by the quantum effects. We show that a passive quantum detector suffers an exponential reduction of detection rate in the worst case. Our numerical results also imply how adversarial attacks affect the performance of quantum radars.
One direction to extend our adversarial quantum detection framework would be to consider various detection frameworks such as side-channel attack detection [17]. It would be also possible to consider hybrid detection models taking advantage of both classical and quantum information as inputs.
References
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5 Appendix
5.1 Proof of Lemma 1
Proof. It suffices to prove that and are equivalent in characterizing the attacker’s generic strategies when observing the hypothesis . Without loss of generality we assume further that is strictly positive, that is for all . We prove two claims as follows.
Claim 1
For every , there always exist a collection of operators such that
(14) |
Proof of Claim 1. Since we assume , we know the distorted density operator has degrees of freedom. Since , we can parameterize them in the basis as well with the coefficients . We can express them in terms of the basis as follows:
(15) |
The impact of the quantum operations upon can be characterized by the change of coordinates of into the ones of under the basis . Now the operations turn the coordinates from into , respectively. Under the representation (or basis) of , we can select the matrix representation of , where as follows:
(16) |
where refers to the row index and column index corresponding to the subscript . The rest of the entries for are all zero. Then it is clear to verify that
(17) |
as claimed.
Also, we have the following conclusion.
Claim 2
Let be a density operator. For every , we can find a such that
(18) |
Proof of claim 2. Our goal is to prove that obtained in (18) meets the requirements of a density operator, that is, it has a trace of ; it is positive definite; it is symmetric (or Hermitian). We first of all notice
(19) | ||||
Noticing that , we conclude
(20) |
Next we prove symmetry of . For convenience we denote as the matrix entries of respectively. It suffices to assume that every matrix is written in the form similar as the one in (16). We want to show that
(21) |
is also symmetric for all choices of . Here is a by matrix where every entry is zero except . By computing entry-wisely, we notice that
(22) |
Similarly,
(23) |
Substituting (22) and (23) into (21) and considering due to symmetry of , we get is indeed symmetric. Since every matrix can be written as a linear combination of , we get that is symmetric for all . As a consequence, as expressed in (14) is symmetric too.
Finally we can show that . Denote . Since , we know by the property of positive definite matrix [18] that are all positive definite matrices for all . Again by the property of closure under additivity we conclude that is a positive definite operator.
Summarizing claim 1 and claim 2, we conclude that it is equivalent to characterize the attacker’s action by or by . Similar arguments can be made regarding and . This concludes the proof of lemma 1.
5.2 Proof of Proposition 1
Proof. We obtain the attacker’s optimal strategies by solving the optimization problem (6). First of all, we can make sure that the optimal strategies exist. Since the objective function is convex in terms of and , We apply first-order conditions by taking partial derivatives of in terms of and set them to be zero, respectively:
(24) | ||||
(25) |
with equality and inequality constraints, which are requirements for being density operators, as follows:
(26) | ||||
By solving (24) and (25) we obtain
(27) | ||||
where are normalization constants. Referring to the equality constraints we conclude that and and arrive at the the solution in (8)(9).
5.3 Proof of Proposition 2
We make the following assumptions:
Assumption 1
We assume the following conditions in proposition 2:
-
1.
and arranges in a strictly descending order (every eigenvalue is algebraically simple) in terms of ;
-
2.
The basis is orthonormal;
-
3.
The eigenstates are orthonormal; The eigenstates are also arranged in a descending order regarding the eigenvalues of ; The eigenvalues are positive whenever ;
-
4.
The difference between eigenvalues are much larger than the projection:
(28)
The assumptions above make sense since is positive definite, symmetric operator. Also, the detector’s optimal strategy is a projection operator with finite rank. We can now state the proof.
Proof. We adopt the classic perturbation theory for symmetric finite-dimensional linear operators [19]. Let be the eigenstates of the operator with eigenvalue . Then we can write
(29) |
Therefore
(30) | ||||
On the other hand, we know
(31) | ||||
Using the theories of perturbation, consider as the unperturbed operator, as the perturbation, with controlling the amplitude of the perturbation. Then the eigenvectors, under assumption 1, can be written as a series of as follows:
(32) |
As a consequence,
(33) | ||||
We can also write out the perturbed eigenvalue in terms of the unperturbed eigenvalue as well as the series of the scale as follows:
(34) |
As a result,
(35) |
For sufficiently small the higher order term vanishes. We get that for all and ,
(36) | ||||
Denote
(37) | ||||
Noticing that . Also if for some , is large, then we must have . By generalization of AM-GM inequality we conclude that
(38) |
Noticing the expression of in (31) and in (30), we get
(39) |
On the other hand, for all . We also notice
(40) |
As a result, we have
(41) |
which concludes the proof.